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REVIEW 4 major objections 6 minor 1 cited by

AuthGuard: Generalizable Deepfake Detection via Language Guidance

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read AuthGuard claims that coupling a deepfake detector with language-describable commonsense artifacts and statistical traces lets a single model generalize to unseen forgery methods and explain its verdicts.

desk verdict A plausible new training recipe for deepfake detection with real OOD gains, but the numbers don't all add up and the language-guidance mechanism is weaker than the abstract suggests. read the letter →

arxiv 2506.04501 v1 pith:EUFVE7X6 submitted 2025-06-04 cs.CV

Guangyu Shen , Zhihua Li , Xiang Xu , Tianchen Zhao , Zheng Zhang , Dongsheng An , Zhuowen Tu , Yifan Xing
show 1 more author
Qin Zhang
This is my paper · ORCID
classification cs.CV
keywords deepfakedetectionout-of-distributiongeneralizationvision-languagecontrastivelearningmultimodallargelanguagemodelspseudo-textgenerationprobabilisticembeddingsreasoningimage-textalignment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

AuthGuard targets a known failure of deepfake detectors: models trained on one generation method lose accuracy when new forgery methods appear, because they rely on pixel-level statistics specific to the training generator. The paper's proposal is to train a specialist vision encoder on two complementary signals at once: standard binary classification over real/fake labels, and image-text contrastive learning against 114k automatically written captions that explain in words why a face looks real or fake. To keep noisy multimodal-model captions from hurting alignment, embeddings are treated as probabilistic distributions, and an adaptive adapter blends the language-guided commonsense features with the statistical features for each image. The resulting encoder is then plugged into a LLaVA-style language model, giving one model that both detects and explains. If the reported numbers hold, a lightweight model trained only on FF++ can generalize to unseen forgeries, with a 6.15% relative AUC gain on DFDC and a 16.68% relative AUC gain on DF40, while also improving deepfake reasoning on DD-VQA by 24.69%.

What carries the argument

The central mechanism is contrastive regularized representation learning driven by automatically generated language. A generalist multimodal model, Llama 3.2, is prompted with the ground-truth real/fake label to explain why a face looks real or fake, yielding 114k image-text pairs whose sentences are split by facial-landmark keywords. Training combines binary cross-entropy on an aggregated embedding with a vision-language contrastive loss on a probabilistic embedding, modeled as a Gaussian with predicted mean and variance and sampled via reparameterization, so that noisy captions are absorbed rather than memorized. An input-dependent adapter with softmax gating merges the contrastive commonsense stream and a self-attention-transformed statistical stream, and the aggregated class token is concatenated with patch tokens and projected into a Vicuna language model for instruction-tuned reasoning.

What would settle it

Run the same training pipeline with the pseudo-text stream corrupted, for example by swapping captions between real and fake images or replacing them with generic scene descriptions, and measure DFDC and DF40 AUC. If the 6.15% and 16.68% out-of-distribution gains survive, the reported generalization is not driven by the language guidance.

Watch

Extended reading notes

Core claim

AuthGuard's central claim is that language guidance is what makes deepfake detection generalize: when the vision encoder is trained to align images with label-conditioned multimodal-model captions describing commonsense facial artifacts, it learns a representation that transfers to unseen forgery methods, rather than only the generator-specific statistics captured by ordinary classifiers. The paper reports that this representation, combined with an adaptive adapter and a classification head, reaches 98.87% AUC on FF++ in-distribution, 78.13% AUC on DFDC, and 91.16% average AUC on eight unseen DF40 face-swapping methods, gains of 6.15% and 16.68% over the best baselines. Used as the vision module of a LLaVA-style language model, the same encoder achieves 90.84% detection accuracy on DD-VQA and raises the average explanation-quality metric by 24.69% over the BLIP-TI expert baseline, establishing the unified detection-and-reasoning claim.

Load-bearing premise

The load-bearing premise is that the captions the multimodal model writes, prompted with the true real/fake label, describe authentic and transferable facial artifacts rather than hallucinated details or phrases that only match the training dataset.

Editorial extensions

If this is right

  • A detector trained only on labeled FF++ frames can raise out-of-distribution AUC by 6.15% on DFDC and 16.68% on DF40 without seeing any test-time forgery method.
  • The same model returns a verdict and artifact-level explanation in one pass, so detection and interpretability no longer require separate systems.
  • Since captions are generated automatically from labels, the recipe scales to new labeled deepfake corpora without human annotation.
  • The class token from the expert encoder is injected into the language model along with patch tokens, improving consistency between the yes/no decision and the reasoning output.
  • Known-attack performance is preserved: FF++ AUC is 98.87% versus 98.12% for the strongest baseline, so the generalization gain is not bought by sacrificing in-distribution accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editors' inference: If label-conditioned captions are the active ingredient, then swapping in a stronger captioning multimodal model should further lift out-of-distribution AUC without changing the vision architecture, making caption quality a direct scaling lever.
  • Editors' inference: The same contrastive-regularized recipe could be pointed at other forensic tasks, such as splice localization or synthetic audio, where artifact vocabulary is partially language-describable, but the paper offers no evidence for those domains.
  • Editors' inference: The adapter's per-image gating weights are a diagnostic window: if the largest out-of-distribution gains occur on images where the gate favors the statistical stream, the commonsense-transfer interpretation would need revision.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. AuthGuard is a deepfake detection and reasoning framework that trains an expert vision encoder by combining binary classification with image-text contrastive learning. The text pairs are pseudo-captions generated by Llama 3.2 using prompts that include the ground-truth real/fake label. The encoder also uses probabilistic embeddings to mitigate text noise and an adaptive adapter to fuse statistical and 'commonsense' features. The vision encoder is then integrated into a LLaVA-style LLM for reasoning and explanation. The paper reports state-of-the-art in-distribution performance on FF++, improved cross-dataset generalization on DFDC and DF40, and large gains on the DD-VQA reasoning benchmark.

Significance. If the empirical results hold, the work is significant: it suggests a practical recipe (MLLM-generated pseudo-text, contrastive alignment, uncertainty-aware embeddings, adaptive fusion) for improving out-of-distribution deepfake detection, and it unifies detection with natural-language reasoning. Strengths include evaluation on external OOD datasets (DFDC, DF40) that are not used in training, a component-wise ablation, and qualitative attention-map and reasoning examples. However, the central mechanistic claim—that language guidance captures transferable commonsense artifacts—is undermined by the label-conditioned prompt used to generate the pseudo-text, and the headline numbers are not recoverable from the reported tables. These issues are load-bearing for the paper's main claims and require additional experiments and corrections.

major comments (4)
  1. [Sec. 3.1, Eq. (3)] The pseudo-text used for contrastive alignment is generated by prompting Llama 3.2 with the ground-truth label in the prompt ('... make this image look Type'), as stated in Sec. 3.1. The text is therefore a class-conditional justification rather than an independent description of visible, transferable artifacts. The claim that the OOD improvements are driven by 'commonsense' language guidance (Sec. 3.2, Conclusion) is not supported without a control that removes the label from the caption-generation step. Please add a comparison in which the MLLM is prompted label-free (e.g., 'Describe this face') or in which the text is verified by human raters for accuracy on held-out images. This is necessary to rule out the alternative that the contrastive branch simply acts as a label-consistent regularizer that overfits to FF++-specific phrasing.
  2. [Abstract, Sec. 4.2, Table 1] The headline gains are not recoverable from the reported tables. The abstract and Sec. 4.2 state a 6.15% AUC gain on DFDC, but Table 1 shows the best baseline AUC is 73.60 (LSDA) against AuthGuard's 78.13, a 4.53-point gain; the 6.15% figure matches the ACC column (UCF 65.75 vs AuthGuard 71.93). Similarly, the claimed 16.68% improvement on DF40 is not directly recoverable from Table 2, where the largest average AUC gain is 13.67 points and the largest average ACC gain is 14.41 points. Please correct the metric labels and specify exactly which pairwise comparison yields each stated gain.
  3. [Sec. 4.1, Table 2] The experimental setup says 8 unseen face-swapping methods from DF40 are selected, but Table 2 reports only 6 methods. Please list all 8 methods, report the results for the omitted ones, or justify why the table is restricted to 6. The reported average and the claimed gain depend on this subset, so the discrepancy affects the verifiability of the main OOD result.
  4. [Sec. 2, Sec. 4.2 (Tables 1-2)] AntiFakePrompt [7] is a vision-language deepfake detector and is discussed in Related Work, but it is not included in any experimental comparison. Since the paper claims a state-of-the-art result among language-guided detection methods, a direct comparison with AntiFakePrompt (or at least a discussion of why it is excluded) is needed to support that claim.
minor comments (6)
  1. [Sec. 4.1, Metrics] The metric name 'BLUE-4' should be 'BLEU-4', and the phrase 'exsisting worsk' should be corrected to 'existing work'.
  2. [Sec. 4.1, Datasets] The first mention of the DD-VQA dataset is cited as [54], but the correct reference appears to be [58] (Common Sense Reasoning for Deep Fake Detection).
  3. [Eq. (3)] The equation uses 'ew·' notation, which presumably denotes e^{ω·} with temperature ω; please make the notation consistent with the text that defines ω.
  4. [Sec. 3.2] The adapter is described as projecting v_i to a '1-D vector' and then applying Softmax to obtain two weights; a 1-D vector would yield only one scalar, so the projection dimension should be clarified as 2.
  5. [Table 1] The table headers mix AUC and ACC columns; the text should explicitly state that both metrics are reported and which column corresponds to each comparison.
  6. [Sec. 4.2, Tables 1-2] Reporting means and standard deviations over multiple random seeds would strengthen the results, especially where margins are small (e.g., 0.76% on FF++).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported cross-dataset gains are measured on external benchmarks with no fitted parameter encoding the test labels.

full rationale

AuthGuard's derivation chain is not circular. The vision encoder is trained on FF++ with pseudo-text generated by Llama 3.2 using the FF++ ground-truth label as prompt context (Sec. 3.1), and the contrastive/classification losses (Eqs. 1-3) optimize only against those training labels. The headline claims are evaluated on DFDC, DF40, and DD-VQA, none of which contribute training signals or fitted parameters, so the reported AUC/accuracy gains are genuine out-of-distribution measurements rather than by-construction reproductions of the inputs. The label-conditioned prompt means the pseudo-text is a class-conditional paraphrase rather than an independent observation, but that is a validity threat to the commonsense-mechanism interpretation, not a circular reduction: no equation in the paper defines the reported metric in terms of a fitted parameter or self-citation. The only self-citations ([49], [59]) are contextual or baseline references and are not load-bearing. Attention-map evidence is model-generated and therefore weak confirmatory evidence, but weakness of evidence is not circularity. Score 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on empirical design choices, MLLM pseudo-text, probabilistic embeddings, and adapter weighting, rather than on new theoretical objects. No new physical or formal entities are introduced.

free parameters (3)
  • Loss weighting alpha and beta = alpha=0.05, beta=1
    The Implementation Details set these values, but the loss equations in Sec. 3.2 and 3.3 do not define them. They are hand-chosen constants that can affect the balance between classification and contrastive objectives.
  • Contrastive temperature omega = not stated
    Equation (3) includes a temperature parameter that controls the sharpness of the contrastive loss; its value is not reported or ablated.
  • DF40 evaluation subset = six methods reported, eight stated
    The paper chooses a subset of DF40 unseen methods for the headline DF40 result, but the selection criterion is not described and the count in the text differs from the table. This choice can alter the reported average.
assumptions (4)
  • domain assumption MLLM-generated pseudo-text, prompted with ground-truth labels, is accurate enough to teach transferable commonsense artifacts.
    Invoked in Sec. 3.1; if the texts are hallucinated or specific to FF++, the contrastive alignment will not transfer to DFDC and DF40.
  • ad hoc to paper Vision-language contrastive alignment on pseudo-text captures representations that generalize to unseen forgery methods.
    This is the central premise of the method and is supported only by the reported empirical results, not by an independent theoretical argument.
  • domain assumption LAION-pretrained ViT-L/14 and frozen RoBERTa encoders are suitable starting points for deepfake-specific learning.
    Stated in Sec. 4.1 without ablation of alternative backbones.
  • domain assumption DeepfakeBench baselines and pre-trained weights are used correctly and are comparable to the proposed method.
    Sec. 4.1 states that baselines come from DeepfakeBench; the results depend on the correctness of that toolchain.

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Cite this review

Pith. "Pith review of AuthGuard: Generalizable Deepfake Detection via Language Guidance." pith.science (2026). https://pith.science/paper/EUFVE7X6

@misc{pith2026250604501,
  author       = {Pith},
  title        = {Pith review of: AuthGuard: Generalizable Deepfake Detection via Language Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EUFVE7X6}},
  note         = {Machine review of arXiv:2506.04501}
}
read the original abstract

Existing deepfake detection techniques struggle to keep-up with the ever-evolving novel, unseen forgeries methods. This limitation stems from their reliance on statistical artifacts learned during training, which are often tied to specific generation processes that may not be representative of samples from new, unseen deepfake generation methods encountered at test time. We propose that incorporating language guidance can improve deepfake detection generalization by integrating human-like commonsense reasoning -- such as recognizing logical inconsistencies and perceptual anomalies -- alongside statistical cues. To achieve this, we train an expert deepfake vision encoder by combining discriminative classification with image-text contrastive learning, where the text is generated by generalist MLLMs using few-shot prompting. This allows the encoder to extract both language-describable, commonsense deepfake artifacts and statistical forgery artifacts from pixel-level distributions. To further enhance robustness, we integrate data uncertainty learning into vision-language contrastive learning, mitigating noise in image-text supervision. Our expert vision encoder seamlessly interfaces with an LLM, further enabling more generalized and interpretable deepfake detection while also boosting accuracy. The resulting framework, AuthGuard, achieves state-of-the-art deepfake detection accuracy in both in-distribution and out-of-distribution settings, achieving AUC gains of 6.15% on the DFDC dataset and 16.68% on the DF40 dataset. Additionally, AuthGuard significantly enhances deepfake reasoning, improving performance by 24.69% on the DDVQA dataset.

Figures

Figures reproduced from arXiv: 2506.04501 by the authors.

Figure 1
Figure 1. (a) Commercial models perform significantly worse [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The framework of AuthGuard comprises two main components: (a) and (b) for expert vision representation learning, and (c) for [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Automatic pseudo-text generation: comparing Llama [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Example of instruction-tuning data generated from im [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: To qualitatively demonstrate the alignment between the vision representations from our expert vision encoder and the paired text, [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Reasoning examples of our method on DD-VQA. Highlighted text indicates the accurate descriptions of facial features. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Evaluating deepfake images beyond FF++: the images with blue-shaded texts are real; the remaining images are AI-generated. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Forward citations

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

59 extracted references · 23 canonical work pages · cited by 1 Pith paper

  1. [7]

    Antifakeprompt: Prompt-tuned vision-language models are fake image detectors.arXiv preprint arXiv:2310.17419,

    You-Ming Chang, Chen Yeh, Wei-Chen Chiu, and Ning Yu. Antifakeprompt: Prompt-tuned vision-language models are fake image detectors.arXiv preprint arXiv:2310.17419,

    arXiv

  2. [1]

    Phi-3 technical report: A highly capable language model locally on your phone.arXiv preprint arXiv:2404.14219, 2024

    Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al. Phi-3 technical report: A highly capable language model locally on your phone.arXiv preprint arXiv:2404.14219, 2024. 6, 7

    arXiv 2024

  3. [2]

    An analysis of recent advances in deep- fake image detection in an evolving threat landscape.arXiv preprint arXiv:2404.16212, 2024

    Sifat Muhammad Abdullah, Aravind Cheruvu, Shravya Kanchi, Taejoong Chung, Peng Gao, Murtuza Jadliwala, and Bimal Viswanath. An analysis of recent advances in deep- fake image detection in an evolving threat landscape.arXiv preprint arXiv:2404.16212, 2024. 2

    arXiv 2024

  4. [3]

    Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774,

    arXiv

  5. [4]

    Meteor: An automatic metric for mt evaluation with improved correlation with hu- man judgments

    Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluation with improved correlation with hu- man judgments. InProceedings of the acl workshop on in- trinsic and extrinsic evaluation measures for machine trans- lation and/or summarization, pages 65–72, 2005. 5

    2005

  6. [5]

    End-to-end reconstruction- classification learning for face forgery detection

    Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, and Xiaokang Yang. End-to-end reconstruction- classification learning for face forgery detection. InProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4113–4122, 2022. 6

    work page 2022

  7. [6]

    Data uncertainty learning in face recognition

    Jie Chang, Zhonghao Lan, Changmao Cheng, and Yichen Wei. Data uncertainty learning in face recognition. InPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5710–5719, 2020. 4

    work page 2020

  8. [8]

    Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

    Hila Chefer, Shir Gur, and Lior Wolf. Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 397–406,

    work page

Show all 59 references
  1. [9]

    Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection

    Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, and Jue Wang. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. InPro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18710–18719, 2022. 2

    2022

  2. [10]

    Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

    Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks. InPro- ceedings of the IEEE/CVF Conference on Computer ...

    2024

  3. [11]

    Gonzalez, Ion Stoica, and Eric P

    Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhang- hao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yong- hao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023. 5

    2023

  4. [12]

    Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna

    Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.See https://vicuna. lmsys. org (accessed 14 April 2023), 2(3):6,

    2023

  5. [13]

    The deepfake detection challenge (dfdc) dataset.arXiv preprint arXiv:2006.07397, 2020

    Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. The deepfake detection challenge (dfdc) dataset.arXiv preprint arXiv:2006.07397, 2020. 2, 5, 6

    2006 arXiv

  6. [14]

    Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020. 1

    2020

  7. [15]

    Deepfake detection by human crowds, machines, and machine-informed crowds.Proceedings of the National Academy of Sciences, 119(1):e2110013119, 2022

    Matthew Groh, Ziv Epstein, Chaz Firestone, and Rosalind Picard. Deepfake detection by human crowds, machines, and machine-informed crowds.Proceedings of the National Academy of Sciences, 119(1):e2110013119, 2022. 2

    2022

  8. [16]

    Denoising diffu- sion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models. pages 6840–6851, 2020. 1

    2020

  9. [17]

    Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models.arXiv preprint arXiv:2106.09685, 2021. 5

    2021 arXiv

  10. [18]

    Implicit identity driven deepfake face swapping detection

    Baojin Huang, Zhongyuan Wang, Jifan Yang, Jiaxin Ai, Qin Zou, Qian Wang, and Dengpan Ye. Implicit identity driven deepfake face swapping detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4490–4499, 2023. 2

    2023

  11. [19]

    Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114, 2013

    Diederik P Kingma. Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114, 2013. 4

    2013 arXiv

  12. [20]

    Deepfake detection: humans vs

    Pavel Korshunov and S ´ebastien Marcel. Deepfake detection: humans vs. machines.arXiv preprint arXiv:2009.03155,

    2009 arXiv

  13. [21]

    Llava-onevision: Easy visual task transfer

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326, 2024. 6, 7

    2024 arXiv

  14. [22]

    Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

    Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. InInterna- tional conference on machine learning, pages 12888–12900. PMLR, 2022. 2

    2022

  15. [23]

    Face x-ray for more gen- eral face forgery detection

    Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more gen- eral face forgery detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5001–5010, 2020. 1, 2, 5, 6

    2020

  16. [24]

    Celeb-df: A large-scale challenging dataset for deep- fake forensics

    Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deep- fake forensics. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3207– 3216, 2020. 2

    2020

  17. [25]

    Rouge: A package for automatic evaluation of summaries

    Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. InText summarization branches out, pages 74–81, 2004. 5

    2004

  18. [26]

    Spatial- phase shallow learning: rethinking face forgery detection in frequency domain

    Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, and Nenghai Yu. Spatial- phase shallow learning: rethinking face forgery detection in frequency domain. InProceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pag...

    2021

  19. [27]

    Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023. 3

    2023

  20. [28]

    Improved baselines with visual instruction tuning

    Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296–26306, 2024. 2, 3, 5, 6, 7

    2024

  21. [29]

    Roberta: A robustly optimized bert pretraining ap- proach.arXiv preprint arXiv:1907.11692, 2019

    Y Liu. Roberta: A robustly optimized bert pretraining ap- proach.arXiv preprint arXiv:1907.11692, 2019. 5

    1907 arXiv

  22. [30]

    Ursa: Under- standing and verifying chain-of-thought reasoning in multi- modal mathematics.arXiv preprint arXiv:2501.04686, 2025

    Ruilin Luo, Zhuofan Zheng, Yifan Wang, Yiyao Yu, Xinzhe Ni, Zicheng Lin, Jin Zeng, and Yujiu Yang. Ursa: Under- standing and verifying chain-of-thought reasoning in multi- modal mathematics.arXiv preprint arXiv:2501.04686, 2025. 1

    2025

  23. [31]

    Gener- alizing face forgery detection with high-frequency features

    Yuchen Luo, Yong Zhang, Junchi Yan, and Wei Liu. Gener- alizing face forgery detection with high-frequency features. InProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 16317–16326, 2021. 1, 5, 6

    2021

  24. [32]

    Llama 3.2: Revolutionizing edge AI and vision with open, customizable models, 2024

    Meta AI Research. Llama 3.2: Revolutionizing edge AI and vision with open, customizable models, 2024. Accessed: 2024-11-05. 3, 6, 8

    2024

  25. [33]

    Detect fakes: Overview, n.d

    MIT Media Lab. Detect fakes: Overview, n.d. Accessed: [2024-11-05]. 2

    2024

  26. [34]

    Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake de- tection

    Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem, Enjie Ghorbel, and Djamila Aouada. Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake de- tection. InProceedings of the IEEE/CVF Conference on ...

    2024

  27. [35]

    Core: Consistent repre- sentation learning for face forgery detection

    Yunsheng Ni, Depu Meng, Changqian Yu, Chengbin Quan, Dongchun Ren, and Youjian Zhao. Core: Consistent repre- sentation learning for face forgery detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12–21, 2022. 6

    2022

  28. [36]

    X-instructblip: A frame- work for aligning x-modal instruction-aware representations to llms and emergent cross-modal reasoning.arXiv preprint arXiv:2311.18799, 2023

    Artemis Panagopoulou, Le Xue, Ning Yu, Junnan Li, Dongxu Li, Shafiq Joty, Ran Xu, Silvio Savarese, Caiming Xiong, and Juan Carlos Niebles. X-instructblip: A frame- work for aligning x-modal instruction-aware representations to llms and emergent cross-modal reasoning.arXiv prep...

    2023 arXiv

  29. [37]

    Bleu: a method for automatic evaluation of machine translation

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. InProceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318,

  30. [38]

    Deepfake generation and detection: A benchmark and survey.arXiv preprint arXiv:2403.17881, 2024

    Gan Pei, Jiangning Zhang, Menghan Hu, Zhenyu Zhang, Chengjie Wang, Yunsheng Wu, Guangtao Zhai, Jian Yang, Chunhua Shen, and Dacheng Tao. Deepfake generation and detection: A benchmark and survey.arXiv preprint arXiv:2403.17881, 2024. 2

    2024

  31. [39]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023. 8

    2023 arXiv

  32. [40]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. InInternational conference on machine learning, p...

    2021

  33. [41]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1

    2022

  34. [42]

    Faceforen- sics: A large-scale video dataset for forgery detection in hu- man faces.arXiv preprint arXiv:1803.09179, 2018

    Andreas R ¨ossler, Davide Cozzolino, Luisa Verdoliva, Chris- tian Riess, Justus Thies, and Matthias Nießner. Faceforen- sics: A large-scale video dataset for forgery detection in hu- man faces.arXiv preprint arXiv:1803.09179, 2018. 2, 5, 6

    2018 arXiv

  35. [43]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22500–...

    2023

  36. [44]

    Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- formation Processing Systems, 35:25278–25294, 2022

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- fo...

    2022

  37. [45]

    Probabilistic face embeddings

    Yichun Shi and Anil K Jain. Probabilistic face embeddings. InProceedings of the IEEE/CVF international conference on computer vision, pages 6902–6911, 2019. 2, 4

    2019

  38. [46]

    Detecting deep- fakes with self-blended images

    Kaede Shiohara and Toshihiko Yamasaki. Detecting deep- fakes with self-blended images. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18720–18729, 2022. 1, 2

    2022

  39. [47]

    Instruction tuning on large language models to improve reasoning per- formance.Authorea Preprints, 2024

    Emily Vaillancourt and Christopher Thompson. Instruction tuning on large language models to improve reasoning per- formance.Authorea Preprints, 2024. 3

    2024

  40. [48]

    Cider: Consensus-based image description evalua- tion

    Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. Cider: Consensus-based image description evalua- tion. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 4566–4575, 2015. 5

    2015

  41. [49]

    Principles of designing robust remote face anti-spoofing systems.arXiv preprint arXiv:2406.03684, 2024

    Xiang Xu, Tianchen Zhao, Zheng Zhang, Zhihua Li, Jon Wu, Alessandro Achille, and Mani Srivastava. Principles of designing robust remote face anti-spoofing systems.arXiv preprint arXiv:2406.03684, 2024. 1

    2024 arXiv

  42. [50]

    Tall: Thumbnail layout for deepfake video detection

    Yuting Xu, Jian Liang, Gengyun Jia, Ziming Yang, Yanhao Zhang, and Ran He. Tall: Thumbnail layout for deepfake video detection. InProceedings of the IEEE/CVF interna- tional conference on computer vision, pages 22658–22668,

  43. [51]

    Ucf: Uncovering common features for generalizable deep- fake detection

    Zhiyuan Yan, Yong Zhang, Yanbo Fan, and Baoyuan Wu. Ucf: Uncovering common features for generalizable deep- fake detection. InProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 22412–22423,

  44. [52]

    Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426,

    Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, and Baoyuan Wu. Deepfakebench: A comprehensive benchmark of deepfake detection.arXiv preprint arXiv:2307.01426,

    arXiv

  45. [53]

    Transcending forgery specificity with latent space augmentation for generalizable deepfake detection

    Zhiyuan Yan, Yuhao Luo, Siwei Lyu, Qingshan Liu, and Baoyuan Wu. Transcending forgery specificity with latent space augmentation for generalizable deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 8984–8994, 2024....

    2024

  46. [54]

    Df40: Toward next-generation deepfake detection.arXiv preprint arXiv:2406.13495, 2024

    Zhiyuan Yan, Taiping Yao, Shen Chen, Yandan Zhao, Xinghe Fu, Junwei Zhu, Donghao Luo, Li Yuan, Chengjie Wang, Shouhong Ding, et al. Df40: Toward next-generation deepfake detection.arXiv preprint arXiv:2406.13495, 2024. 2, 5, 6

    2024 arXiv

  47. [55]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models.arXiv preprint arXiv:2308.06721,

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models.arXiv preprint arXiv:2308.06721,

    arXiv

  48. [56]

    Boosting continual learning of vision-language models via mixture-of-experts adapters

    Jiazuo Yu, Yunzhi Zhuge, Lu Zhang, Ping Hu, Dong Wang, Huchuan Lu, and You He. Boosting continual learning of vision-language models via mixture-of-experts adapters. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 23219–23230, 2024. 4

    2024

  49. [57]

    Unified physical-digital attack detection chal- lenge

    Haocheng Yuan, Ajian Liu, Junze Zheng, Jun Wan, Jiankang Deng, Sergio Escalera, Hugo Jair Escalante, Isabelle Guyon, and Zhen Lei. Unified physical-digital attack detection chal- lenge. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, page...

    2024

  50. [58]

    Common sense reasoning for deep fake detection.arXiv preprint arXiv:2402.00126, 2024

    Yue Zhang, Ben Colman, Ali Shahriyari, and Gaurav Bharaj. Common sense reasoning for deep fake detection.arXiv preprint arXiv:2402.00126, 2024. 1, 2, 5, 6, 7, 8

    2024 arXiv

  51. [59]

    Learning self-consistency for deepfake detection

    Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. Learning self-consistency for deepfake detection. InProceedings of the IEEE/CVF international conference on computer vision, pages 15023–15033, 2021. 1, 2

    2021

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.