Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-10T03:28:57.522947Z
Paper Citation Record · LEDGER
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
As of 10 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2607.08674.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T00:29:28.781414Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
73 of 73 outbound references displayed
- verified exact5
- verified fuzzy64
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8f1acebf-38c3-43b8-b83c-2fd16e6c92cc · outbound
This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Progressive Growing of GANs for Improved Quality, Stability, and Variation
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T03:36:44.081535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9b129052-0bb9-429f-9de1-e0b8de087600 · outbound
This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.521494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c8b60e1f-7705-447d-9d9d-2250aea3439b · outbound
This paper cites Taming transformers for high-resolution image synthesis.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Taming transformers for high-resolution image synthesis
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.430944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e2163c91-4c8d-4fc0-93e4-9c3a6c60e1f5 · outbound
This paper cites AI Deepfakes Surge: $200 Million Lost, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection AI Deepfakes Surge: $200 Million Lost, 2025
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.432322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 29b571e2-24e0-471d-97d8-68a327d83ee1 · outbound
This paper cites Deepfake-related fraud forecast to hit $40b by 2027,.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Deepfake-related fraud forecast to hit $40b by 2027,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.439233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fbe991bc-933e-426a-a719-b0db52e56337 · outbound
This paper cites an unresolved cited work.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T03:36:44.435545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 568d24c8-914d-440c-a409-22b06a280fda · outbound
This paper cites A com- prehensive survey, large-scale empirical study, and future in- sights on generalization, robustness, and explainability of ai- generated image detection.SSRN, 2026.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection A com- prehensive survey, large-scale empirical study, and future in- sights on generalization, robustness, and explainability of ai- generated image detection.SSRN, 2026
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.457206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 137c2856-783b-4a47-9d57-bc27e77b058c · outbound
This paper cites Adversarial attacks on audio deep- fake detection: A benchmark and comparative study.Pro- ceedings of the BMVC 2025 Workshop on Secure and Robust Biometrics Systems, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Adversarial attacks on audio deep- fake detection: A benchmark and comparative study.Pro- ceedings of the BMVC 2025 Workshop on Secure and Robust Biometrics Systems, 2025
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.459032Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 6311e736-4234-47e7-a9da-78c6c2d1b4c8 · outbound
This paper cites Leveraging frequency analysis for deep fake image recognition.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Leveraging frequency analysis for deep fake image recognition
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.466769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8534a37d-f87c-4bdf-b6cc-2c7af2db176f · outbound
This paper cites Watch your up-convolution: Cnn based generative deep neural net- works are failing to reproduce spectral distributions.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Watch your up-convolution: Cnn based generative deep neural net- works are failing to reproduce spectral distributions
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.444697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 80d1f27e-7b1b-4105-88b8-3e1eceede377 · outbound
This paper cites Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.451261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 48f15d90-6323-4912-8cc3-7aa26e201384 · outbound
This paper cites Diversity matters: Dataset diversification and dual-branch network for generalized ai-generated image detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Diversity matters: Dataset diversification and dual-branch network for generalized ai-generated image detection
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.460733Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f40fde09-f11e-4b05-9660-b30c32da8eda · outbound
This paper cites Sheild: A secure and highly enhanced integrated learning for robust deepfake detection against adversarial attacks.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Sheild: A secure and highly enhanced integrated learning for robust deepfake detection against adversarial attacks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.478973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation aded9390-78ca-4bbb-9f42-03ae4e150e13 · outbound
This paper cites Deep learning-based counter anti-forensic of gan-based attack in hevc compressed domain using coding pattern analysis.Ex- pert Systems with Applications, 233:120912, 2023.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Deep learning-based counter anti-forensic of gan-based attack in hevc compressed domain using coding pattern analysis.Ex- pert Systems with Applications, 233:120912, 2023
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.448819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f581d4c0-33d9-43a5-be74-fbeae0bbba8e · outbound
This paper cites Towards uni- versal fake image detectors that generalize across genera- tive models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Towards uni- versal fake image detectors that generalize across genera- tive models
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.452989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2c6c2c0d-34c5-4865-8b99-e8c95baea01e · outbound
This paper cites Raising the bar of ai-generated image detection with clip.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Raising the bar of ai-generated image detection with clip
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.462998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 306f5080-d35e-4a29-bef1-6bc39e0b116f · outbound
This paper cites C2p-clip: Inject- ing category common prompt in clip to enhance generaliza- tion in deepfake detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection C2p-clip: Inject- ing category common prompt in clip to enhance generaliza- tion in deepfake detection
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.400524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a4ee5098-11f8-4684-9a16-c0cdf633c6b2 · outbound
This paper cites Face2parts: Exploring coarse-to-fine inter-regional facial de- pendencies for generalized deepfake detection.IEEE Access, 14:55111–55125, 2026.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Face2parts: Exploring coarse-to-fine inter-regional facial de- pendencies for generalized deepfake detection.IEEE Access, 14:55111–55125, 2026
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.480996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c223a612-094e-4b37-b4f8-b340f4668c3b · outbound
This paper cites A robust open-set multi-instance learning for defending adversarial attacks in digital image.IEEE Trans- actions on Information Forensics and Security, 19:2098– 2111, 2023.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection A robust open-set multi-instance learning for defending adversarial attacks in digital image.IEEE Trans- actions on Information Forensics and Security, 19:2098– 2111, 2023
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.497418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e7b730b3-8b69-42db-a341-d1bc160824c0 · outbound
This paper cites Counter- act against gan-based attacks: A collaborative learning ap- proach for anti-forensic detection.Applied Soft Computing, 153:111287, 2024.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Counter- act against gan-based attacks: A collaborative learning ap- proach for anti-forensic detection.Applied Soft Computing, 153:111287, 2024
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.475780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4113ff5a-6073-4e75-b0f5-fde121ee6a94 · outbound
This paper cites an unresolved cited work.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T03:36:44.433911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2f3117a9-9ad2-4d67-b831-644a4a752c7b · outbound
This paper cites Grex-bench: Bench- marking generalization, robustness, and explainability in ai- generated image detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Grex-bench: Bench- marking generalization, robustness, and explainability in ai- generated image detection
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.509272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ace669fb-8176-4ad3-807d-f53ed8fc270d · outbound
This paper cites Trace: Training-free partial audio deepfake detection via em- bedding trajectory analysis of speech foundation models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Trace: Training-free partial audio deepfake detection via em- bedding trajectory analysis of speech foundation models
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.505508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9abf87de-a264-416a-8d00-746a09dd42f1 · outbound
This paper cites Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T03:36:44.065007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 30a7076a-c73a-4d4d-bd19-40f15c4c9449 · outbound
This paper cites Advbench: A comprehensive benchmark of adver- sarial attacks on deepfake detectors in real-world consumer applications.Authorea Preprints, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Advbench: A comprehensive benchmark of adver- sarial attacks on deepfake detectors in real-world consumer applications.Authorea Preprints, 2025
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.517907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation fc8c73a0-5806-407a-afe9-5ebb764e16ed · outbound
This paper cites Adversarial perturbations fool deepfake detectors.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Adversarial perturbations fool deepfake detectors
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.402394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8917056e-cd27-4a2e-93fa-91b9feb1fde8 · outbound
This paper cites Gan-generated image detection with self-attention mecha- nism against gan generator defect.IEEE Journal of Selected Topics in Signal Processing, 14(5):969–981, 2020.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Gan-generated image detection with self-attention mecha- nism against gan generator defect.IEEE Journal of Selected Topics in Signal Processing, 14(5):969–981, 2020
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.462724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 526cbb9a-656a-41d5-af43-baa20f62df6f · outbound
This paper cites Improved denoising diffusion probabilistic models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Improved denoising diffusion probabilistic models
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.477381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 20f8eefb-c168-4ac7-bd8c-9be245ddf179 · outbound
This paper cites Reasoning with neural tensor networks for knowledge base completion.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Reasoning with neural tensor networks for knowledge base completion
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.482674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d7c98bee-9435-4f71-8d9f-deba6ace9aa4 · outbound
This paper cites Atten- tion is all you need.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Atten- tion is all you need
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.489781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 361bd4f0-a66c-4c13-8284-5b9322dfb99a · outbound
This paper cites Perception Encoder: The best visual embeddings are not at the output of the network.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Perception Encoder: The best visual embeddings are not at the output of the network
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T03:36:44.065159Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 748711fa-c152-420c-b1c0-c9aa97ea8b29 · outbound
This paper cites LoRA: Low-Rank Adaptation of Large Language Models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection LoRA: Low-Rank Adaptation of Large Language Models
Reference 32
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T03:36:44.075277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9efc4c18-ee54-479e-af96-d68abac30c17 · outbound
This paper cites Cnn-generated images are surprisingly easy to spot.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Cnn-generated images are surprisingly easy to spot
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.519702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b6422c24-7a07-4c82-a849-d2d8afc3ef65 · outbound
This paper cites Frequency-aware deepfake de- tection: Improving generalizability through frequency space domain learning.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Frequency-aware deepfake de- tection: Improving generalizability through frequency space domain learning
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.443576Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bf65188d-d45c-403c-8e64-54da32704fdd · outbound
This paper cites Learning on gradients: Generalized arti- facts representation for gan-generated images detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Learning on gradients: Generalized arti- facts representation for gan-generated images detection
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.468387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 65c67fb0-513c-4e39-8a62-55e071181991 · outbound
This paper cites Detecting GAN generated Fake Images using Co-occurrence Matrices.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Detecting GAN generated Fake Images using Co-occurrence Matrices
Reference 36
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T03:36:44.078800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5b8559ab-6b50-4dca-9bfa-d8da340da37d · outbound
This paper cites On the detection of synthetic images generated by diffusion mod- els.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection On the detection of synthetic images generated by diffusion mod- els
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.441070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3b291e37-999d-4bc3-9668-4da3b52857d9 · outbound
This paper cites What makes fake images detectable? understanding prop- erties that generalize.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection What makes fake images detectable? understanding prop- erties that generalize
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.491311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation bba70aeb-cac3-4736-a48a-e7b52fd7b827 · outbound
This paper cites Thinking in frequency: Face forgery detection by min- ing frequency-aware clues.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Thinking in frequency: Face forgery detection by min- ing frequency-aware clues
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.484377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 6f426362-8272-4312-80bd-ab0b2d7a8334 · outbound
This paper cites Forgery-aware adaptive transformer for generalizable synthetic image detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Forgery-aware adaptive transformer for generalizable synthetic image detection
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.487934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation d7f14cad-6ed2-4e05-b7a3-2e2b1db35311 · outbound
This paper cites Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.486074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 217c30e2-dcbb-44bd-a797-aaf842c858f2 · outbound
This paper cites Forgelens: Data- efficient forgery focus for generalizable forgery image detec- tion, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Forgelens: Data- efficient forgery focus for generalizable forgery image detec- tion, 2025
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.468539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 855227c5-39d8-4e47-b606-5ed5e8355a3e · outbound
This paper cites Effort: Efficient orthogonal mod- eling for generalizable ai-generated image detection.Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2, 2024.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Effort: Efficient orthogonal mod- eling for generalizable ai-generated image detection.Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2, 2024
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.470182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ff0caef2-28c7-49e5-b8ca-24a13513c3be · outbound
This paper cites Dire for diffusion-generated image detection.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Dire for diffusion-generated image detection
Reference 44
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- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.489601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 47e7be10-3089-4380-aa00-b0a777adba8d · outbound
This paper cites Aligned datasets improve detection of latent diffusion-generated images.Proceedings of the Inter- nation Conference on Learning Representations, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Aligned datasets improve detection of latent diffusion-generated images.Proceedings of the Inter- nation Conference on Learning Representations, 2025
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.406529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0be63bb0-91e5-4fc1-88ca-c7c4bb05d43d · outbound
This paper cites Stay-positive: A case for ignoring real image features in fake image detec- tion.Proceedings of the Internation Conference on Machine Learning, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Stay-positive: A case for ignoring real image features in fake image detec- tion.Proceedings of the Internation Conference on Machine Learning, 2025
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.491519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 13ee4ff6-ea5f-4b73-9251-26b73725113b · outbound
This paper cites To- wards real-world blind face restoration with generative facial prior.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection To- wards real-world blind face restoration with generative facial prior
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.516114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation be7bd391-fba0-4f7f-8e2e-26ebc87064c2 · outbound
This paper cites Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.413861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 00a772ec-dc91-4125-9e57-b6fe480b19d8 · outbound
This paper cites FFDNet: Toward a fast and flexible solution for CNN-based im- age denoising.IEEE Transactions on Image Processing, 27(9):4608–4622, 2018.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection FFDNet: Toward a fast and flexible solution for CNN-based im- age denoising.IEEE Transactions on Image Processing, 27(9):4608–4622, 2018
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.423792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 12433042-8837-48a4-961c-abde3def1e57 · outbound
This paper cites EnlightenGAN: Deep light enhancement without paired supervision.IEEE Transactions on Image Process- ing, 30:2340–2349, 2021.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection EnlightenGAN: Deep light enhancement without paired supervision.IEEE Transactions on Image Process- ing, 30:2340–2349, 2021
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.430622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9e2fe038-c870-409b-ae93-91a9a751226a · outbound
This paper cites Towards understanding convergence and generalization of adamw.IEEE transactions on pattern analysis and machine intelligence, 46(9):6486–6493, 2024.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Towards understanding convergence and generalization of adamw.IEEE transactions on pattern analysis and machine intelligence, 46(9):6486–6493, 2024
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.437539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ce449b35-b357-409d-93e7-4708d1a4197b · outbound
This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Unpaired image-to-image translation using cycle- consistent adversarial networks
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.470347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 870d4c06-883c-4005-869a-26a8c39aa02b · outbound
This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Large Scale GAN Training for High Fidelity Natural Image Synthesis
Reference 53
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- verified exact
- local_arxiv, observed 2026-07-10T03:36:44.070655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c397e993-2769-4037-9539-5d307b2bc412 · outbound
This paper cites A style-based generator architecture for generative adversarial networks.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection A style-based generator architecture for generative adversarial networks
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.420007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b0ced1e8-e692-4a26-856d-7d95faa58069 · outbound
This paper cites Semantic image synthesis with spatially-adaptive nor- malization.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Semantic image synthesis with spatially-adaptive nor- malization
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.441826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e39635f4-f9d9-41d5-9a9c-1879e3ad0fd5 · outbound
This paper cites Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.411996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c50da637-a7a6-430c-be7a-1701aae49a93 · outbound
This paper cites Faceforen- sics++: Learning to detect manipulated facial images.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Faceforen- sics++: Learning to detect manipulated facial images
Reference 57
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- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.410295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 93306b5e-ac00-490c-b7c0-3c11c35ef05c · outbound
This paper cites Learning to see in the dark.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Learning to see in the dark
Reference 58
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- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.494820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2f141bac-e271-4b2f-9c32-c9dd63db8c88 · outbound
This paper cites Second-order attention network for single im- age super-resolution.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Second-order attention network for single im- age super-resolution
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.511038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 688bfe26-7414-4ef9-8c77-6df68e6b3409 · outbound
This paper cites Photographic image syn- thesis with cascaded refinement networks.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Photographic image syn- thesis with cascaded refinement networks
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.507393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8da7e5c8-1818-4a8e-b85b-0010b8aafce6 · outbound
This paper cites Diverse image synthesis from semantic layouts via conditional imle.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Diverse image synthesis from semantic layouts via conditional imle
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.512751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation aa7c0c74-9624-4a8a-b9a5-28af1ece5854 · outbound
This paper cites Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.500598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8814eb7e-c77d-48c7-90c4-b0ca46c2c9fa · outbound
This paper cites High-resolution image synthesis with latent diffusion models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection High-resolution image synthesis with latent diffusion models
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.501492Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2c7bb995-8edf-4caa-8eb4-b575a0358481 · outbound
This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Reference 64
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T03:36:44.072817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a5d923b3-e2aa-4a5e-90d1-7093db94a12a · outbound
This paper cites Zero-shot text-to-image generation.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Zero-shot text-to-image generation
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.495101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ee780d01-6425-41ed-99d3-bb37c33b9de1 · outbound
This paper cites Chan, Chongyi Li, and Chen Change Loy.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Chan, Chongyi Li, and Chen Change Loy
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.493248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1b4040e4-e4e6-4fcf-8b56-49b6ff7ed19e · outbound
This paper cites Double compression detection in hevc-coded video with the same coding parameters using picture partitioning information.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Double compression detection in hevc-coded video with the same coding parameters using picture partitioning information
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.503509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation eb1a0dbb-7bb0-468c-b5ea-c7d6ac2d0a5e · outbound
This paper cites Diff- BIR: Towards blind image restoration with generative diffu- sion prior.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Diff- BIR: Towards blind image restoration with generative diffu- sion prior
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.514473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef611b4e-604e-4e8d-867e-08ebb733f3b6 · outbound
This paper cites SDEdit: Guided image synthesis and editing with stochastic differential equa- tions.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection SDEdit: Guided image synthesis and editing with stochastic differential equa- tions
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.473644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef9f4f71-a77b-4eba-9fee-95a8cfb64913 · outbound
This paper cites Towards unsupervised deep image enhancement with generative adversarial network.IEEE Transactions on Image Processing, 29:9140–9151, 2020.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Towards unsupervised deep image enhancement with generative adversarial network.IEEE Transactions on Image Processing, 29:9140–9151, 2020
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.473989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation faca007c-7af2-4621-b81e-d64fd7747bf1 · outbound
This paper cites Anti- forensic against double jpeg compression detection using ad- versarial generative network.In Proceedings of the Korean Society of Broadcast Engineers Conference, pages 58–60,.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Anti- forensic against double jpeg compression detection using ad- versarial generative network.In Proceedings of the Korean Society of Broadcast Engineers Conference, pages 58–60,
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.464850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c0ded9c5-f94a-4d5a-b9d0-738806b84fde · outbound
This paper cites Analysis of generative adversarial network targeting anti-forensic in jpeg compressed domain.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Analysis of generative adversarial network targeting anti-forensic in jpeg compressed domain
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.449537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 59f9f110-db4b-437e-98f5-a088b670effa · outbound
This paper cites Guard: Generative unmasking and adversarial-resistant deepfake detection using multi-model knowledge distillation.Authorea Preprints, 2025.
Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection Guard: Generative unmasking and adversarial-resistant deepfake detection using multi-model knowledge distillation.Authorea Preprints, 2025
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T03:36:44.445444Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
Observation c413f6ff-43ba-4d04-9b91-9f456e868665 · inbound
Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning cites this paper.
Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T00:29:28.781414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.