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arxiv: 2606.21108 · v1 · pith:MFBCR5W3new · submitted 2026-06-19 · 💻 cs.CV

SARIF: Segment Anything for Robust Image Forensics

Dong-Hyun Moon , Ju-Hyeon Nam , Sang-Chul Lee This is my paper

Pith reviewed 2026-06-26 14:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords image forgery localizationsegment anything modelcross-dataset generalizationrobustness to corruptionsfeedback-guided decoderdual-encoder designautomatic segmentationforensic traces
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The pith

SARIF adapts SAM with dual encoders and feedback refinement to localize forgeries automatically across datasets and corruptions.

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

The paper seeks to establish that the Segment Anything Model can be specialized for image forgery localization by adding a dual-encoder structure and a feedback-guided mask decoder. Residual features between an adapted encoder and its frozen counterpart supply forgery cues that are fused with prior mask prompts in a block-wise mechanism, allowing iterative automatic refinement without manual prompts. Extensive benchmark tests are presented as evidence of improved average performance when models are evaluated across different datasets and under common image corruptions. A sympathetic reader would care because existing forgery detectors typically lose accuracy under distribution shifts or image degradations that occur in practice.

Core claim

SARIF introduces a feedback-guided mask decoder and dual-encoder design that extracts forgery-specific information from residual features between an adapted encoder and its frozen counterpart. These residuals are fused with previous mask prompts via block-wise prompting to drive a feedback-based refinement process, enabling automatic forgery segmentation that leverages SAM's promptable architecture and generalization ability.

What carries the argument

Block-wise prompting mechanism that derives forgery-specific cues from residual features between an adapted encoder and its frozen counterpart, fused with previous mask prompts to drive feedback-based mask refinement.

If this is right

  • Automatic forgery segmentation becomes possible without manual prompt input.
  • Strong average performance holds across multiple standard forgery-localization benchmarks.
  • Robustness is observed under common image corruptions such as noise and compression.
  • The approach preserves SAM's broad generalization while adding domain-specific forensic traces.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The residual-feature prompting pattern could be tested on other foundation models to adapt them for specialized detection tasks.
  • Iterative feedback refinement may reduce reliance on manual prompts in segmentation problems beyond forensics.
  • Observed corruption robustness suggests the method could be evaluated on real-world image streams that contain mixed degradations.

Load-bearing premise

Residual features between the adapted encoder and its frozen counterpart reliably supply forgery-specific cues that the feedback decoder can use for accurate automatic segmentations.

What would settle it

Evaluation on a previously unseen forgery dataset containing manipulation types absent from the training distribution, measuring whether cross-dataset average performance remains high relative to prior methods.

Figures

Figures reproduced from arXiv: 2606.21108 by Dong-Hyun Moon, Ju-Hyeon Nam, Sang-Chul Lee.

Figure 1
Figure 1. Figure 1: Motivation. Without the adapter, the encoder focuses on semantic content and misses subtle manipulation artifacts, causing imprecise masks. With the adapter, it learns forgery-specific cues that guide the decoder to produce sharper and more accurate localization. Concretely, this domain gap between the adapted and the frozen encoder repre￾sents the adapter-learned forgery-specific information. To move beyo… view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of the proposed SARIF. (a) Fine-tuned SAM Image Encoder. (b) Original SAM Image Encoder. (c) Feedback-Guided Mask Decoder. (d) Forgery-Specific Information Extractor. (e) Notation description used in this paper. frozen. At each selected transformer block, FSIE computes block-wise residual cues between the adapted and frozen branches to distill forgery-specific infor￾mation, which i… view at source ↗
Figure 3
Figure 3. Figure 3: More details about Feedback-Guided Mask Decoder. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of other methods and SARIF under the CASIAv2 train￾ing scheme. (a) Input images with ground truth. (b) UNet [69]. (c) MantraNet [78]. (d) RRUNet [4]. (e) TransForensic [23]. (f) FBINet [18]. (g) MT-SENet [86]. (h) MVSS￾Net [13]. (i) PIMNet [2]. (j) EITLNet [20]. (k) M2SFormer [61]. (l) SAM [41]. (m) autoSAM [70]. (n) IMDprompter [85]. (o) SAFIRE [44]. (p) SARIF(ours). Green lines den… view at source ↗
Figure 6
Figure 6. Figure 6: Authentic Image Pre￾diction. Left: Input image, Right: Model prediction. gesting that forgery-specific cue extraction and feedback-based mask refinement are complementary. Setting 1 (Vanilla SAM). Directly applying SAM to forgery localization re￾sults in poor performance. Because of the domain gap between natural-object segmentation and image forensics, SAM often fails to attend to manipulation￾specific tr… view at source ↗
Figure 7
Figure 7. Figure 7: Various distortion tests on CASIAv2. (a) Gaussian Noise, severity 1, 2, 3 means σ = 0.1, 0.3, 0.5. (b) Gaussian Blur, severity 1, 2, 3 means σ = 3,5,9. (c) JPEG Compression, severity 1, 2, 3 means q = 100, 50, 10. (d) Mixed robustness test, severity 1-4 correspond to (blur & JPEG), (noise & blur), (noise & JPEG), (noise & blur & JPEG), respectively. For each mixed setting, severity level 2 was used for eve… view at source ↗
read the original abstract

Image forgery localization remains challenging due to diverse manipulation techniques and distribution shifts. Existing forgery localization models achieve high accuracy on benchmarks but often struggle with cross-domain generalization and robustness. In this paper, we propose SARIF (Segment Anything for Robust Image Forensics), a framework that leverages the Segment Anything Model (SAM), which has a promptable architecture and strong generalization ability. SARIF introduces a feedback-guided mask decoder and a dual-encoder design that extracts forgery-specific information to capture forensic traces while exploiting the SAM architecture. To localize manipulated regions, we design a block-wise prompting mechanism that derives forgery-specific cues from residual features between an adapted encoder and its frozen counterpart. These features are fused with the previous mask prompt to drive a feedback-based mask refinement process, enabling automatic forgery segmentation without manual input. Extensive experiments on standard forgery-localization benchmarks show that SARIF achieves strong average cross-dataset performance and robustness to common image corruptions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes SARIF, a framework adapting the Segment Anything Model (SAM) for image forgery localization. It introduces a dual-encoder design that computes residual features between an adapted encoder and its frozen counterpart, fuses these with previous mask prompts via a block-wise prompting mechanism, and feeds them into a feedback-guided mask decoder for automatic (prompt-free) segmentation of manipulated regions. The central claim is that this yields strong average cross-dataset performance on standard forgery-localization benchmarks together with robustness to common image corruptions.

Significance. If the performance claims hold, the work would demonstrate a practical way to transfer SAM’s generalization properties to the forensics domain via residual cues and feedback refinement, potentially improving cross-domain robustness over existing specialized models. The architecture itself is a plausible, parameter-light adaptation of SAM; however, the manuscript supplies no quantitative results, datasets, baselines, or ablations, so the significance cannot be assessed from the given text.

major comments (2)
  1. [Abstract] Abstract: the claim of 'strong average cross-dataset performance and robustness to common image corruptions' is presented without any numerical results, error bars, dataset names, or baseline comparisons, rendering the central empirical claim unverifiable from the manuscript.
  2. [Abstract] Abstract / method description: the key assumption that residual features between the adapted and frozen encoders 'reliably supply forgery-specific cues' is stated but never supported by any derivation, ablation, or visualization; without this link the feedback decoder’s claimed advantage over standard SAM prompting remains ungrounded.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the referee's comments on our manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'strong average cross-dataset performance and robustness to common image corruptions' is presented without any numerical results, error bars, dataset names, or baseline comparisons, rendering the central empirical claim unverifiable from the manuscript.

    Authors: We agree that the abstract's performance claims would be more verifiable with concrete numbers. In the revised manuscript we will insert key quantitative results (average cross-dataset metrics, dataset names, and baseline comparisons) into the abstract while retaining conciseness. revision: yes

  2. Referee: [Abstract] Abstract / method description: the key assumption that residual features between the adapted and frozen encoders 'reliably supply forgery-specific cues' is stated but never supported by any derivation, ablation, or visualization; without this link the feedback decoder’s claimed advantage over standard SAM prompting remains ungrounded.

    Authors: We acknowledge that the current text does not supply supporting evidence for the residual-feature assumption. We will add ablations, visualizations of the residual maps, and a short derivation or justification in the revised method and experimental sections, with a brief reference in the abstract, to establish the link to forgery-specific cues. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper proposes an architectural adaptation of SAM with a dual-encoder residual mechanism and feedback decoder for forgery localization. No equations, derivations, or first-principles predictions appear in the provided text. Claims rest on empirical cross-dataset performance rather than any reduction of outputs to inputs by construction, fitted parameters renamed as predictions, or load-bearing self-citations. The design choices are presented as engineering decisions, not derived results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no free parameters, axioms, or invented entities are explicitly described or quantified.

pith-pipeline@v0.9.1-grok · 5687 in / 1065 out tokens · 20864 ms · 2026-06-26T14:50:12.639726+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

88 extracted references · 10 canonical work pages · 4 internal anchors

  1. [1]

    Heliyon9(10) (2023)

    Ahmed, S., Chua, H.W.: Perception and deception: Exploring individual responses to deepfakes across different modalities. Heliyon9(10) (2023)

    2023

  2. [2]

    Pattern Recog- nition159, 111136 (2025)

    Bai, N., Wang, X., Han, R., Hou, J., Wang, Y., Pang, S.: Pim-net: Progressive inconsistency mining network for image manipulation localization. Pattern Recog- nition159, 111136 (2025)

    2025

  3. [3]

    ACM Trans

    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: A ran- domized correspondence algorithm for structural image editing. ACM Trans. Graph.28(3), 24 (2009)

    2009

  4. [4]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Bi, X., Wei, Y., Xiao, B., Li, W.: Rru-net: The ringed residual u-net for image splic- ing forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    2019

  5. [5]

    Caruso,R.D.,Postel,G.C.:Imageeditingwithadobephotoshop6.0.Radiographics 22(4), 993–1002 (2002)

    2002

  6. [6]

    IEEE Transactions on Circuits and Systems for Video Technology (2024)

    Chen, H., Wei, P., Guo, G., Gao, S.: Sam-cod+: Sam-guided unified framework for weakly-supervised camouflaged object detection. IEEE Transactions on Circuits and Systems for Video Technology (2024)

    2024

  7. [7]

    In: Proceedings of the IEEE/CVF international con- ference on computer vision

    Chen, X., Dong, C., Ji, J., Cao, J., Li, X.: Image manipulation detection by multi- view multi-scale supervision. In: Proceedings of the IEEE/CVF international con- ference on computer vision. pp. 14185–14193 (2021)

    2021

  8. [8]

    IEEE Transactions on Information Forensics and Security9(4), 554–567 (2014)

    Chierchia, G., Poggi, G., Sansone, C., Verdoliva, L.: A bayesian-mrf approach for prnu-based image forgery detection. IEEE Transactions on Information Forensics and Security9(4), 554–567 (2014)

    2014

  9. [9]

    In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

    Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., Barnard, K.: Attentional feature fusion. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 3560–3569 (2021)

    2021

  10. [10]

    IEEE Trans- actions on Information Forensics and Security8(7), 1182–1194 (2013)

    De Carvalho, T.J., Riess, C., Angelopoulou, E., Pedrini, H., de Rezende Rocha, A.: Exposing digital image forgeries by illumination color classification. IEEE Trans- actions on Information Forensics and Security8(7), 1182–1194 (2013)

    2013

  11. [11]

    Philosophy & Technology34(4), 1311–1332 (2021)

    De Ruiter, A.: The distinct wrong of deepfakes. Philosophy & Technology34(4), 1311–1332 (2021)

    2021

  12. [12]

    In: 2009 16th IEEE International Conference on Image Processing (ICIP)

    Dirik, A.E., Memon, N.: Image tamper detection based on demosaicing artifacts. In: 2009 16th IEEE International Conference on Image Processing (ICIP). pp. 1497–1500. IEEE (2009)

    2009

  13. [13]

    IEEE Transactions on Pattern Analysis and Machine Intelligence45(3), 3539–3553 (2022)

    Dong, C., Chen, X., Hu, R., Cao, J., Li, X.: Mvss-net: Multi-view multi-scale super- vised networks for image manipulation detection. IEEE Transactions on Pattern Analysis and Machine Intelligence45(3), 3539–3553 (2022)

    2022

  14. [14]

    IEEE (Jul 2013).https://doi.org/10.1109/chinasip

    Dong, J., Wang, W., Tan, T.: CASIA image tampering detection evaluation database.In:2013IEEEChinaSummitandInternationalConferenceonSignaland Information Processing. IEEE (Jul 2013).https://doi.org/10.1109/chinasip. 2013.6625374,https://doi.org/10.1109/chinasip.2013.6625374 16 D.-H. Moon, J.-H. Nam, and S.-C. Lee

    work page doi:10.1109/chinasip 2013

  15. [15]

    In: In- ternational Conference on Learning Representations (2021),https://openreview

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: In- ternational Conference on Learning Representations (2021),https://openreview. net/forum?id=YicbFdNTTy

    2021

  16. [16]

    IEEE Transactions on Information Forensics and Security7(5), 1566–1577 (2012)

    Ferrara, P., Bianchi, T., De Rosa, A., Piva, A.: Image forgery localization via fine- grained analysis of cfa artifacts. IEEE Transactions on Information Forensics and Security7(5), 1566–1577 (2012)

    2012

  17. [17]

    Pattern Recognition p

    Gao, D., Zhou, Y., Yan, H., Chen, C., Hu, X.: Cod-sam: Camouflage object detec- tion using sam. Pattern Recognition p. 111826 (2025)

    2025

  18. [18]

    IEEE access10, 62751–62762 (2022)

    Gu, A.R., Nam, J.H., Lee, S.C.: Fbi-net: Frequency-based image forgery local- ization via multitask learning with self-attention. IEEE access10, 62751–62762 (2022)

    2022

  19. [19]

    In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion (CVPR)

    Guillaro, F., Cozzolino, D., Sud, A., Dufour, N., Verdoliva, L.: Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization. In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion (CVPR). pp. 20606–20615 (June 2023)

    2023

  20. [20]

    In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

    Guo, K., Zhu, H., Cao, G.: Effective image tampering localization via enhanced transformer and co-attention fusion. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 4895–4899. IEEE (2024)

    2024

  21. [21]

    Advances in neural information processing systems35, 1140–1156 (2022)

    Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z., Cheng, M.M., Hu, S.M.: Segnext: Rethink- ing convolutional attention design for semantic segmentation. Advances in neural information processing systems35, 1140–1156 (2022)

    2022

  22. [22]

    IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    Han, R., Wang, X., Bai, N., Wang, Y., Hou, J., Xue, J.: Hdf-net: Capturing ho- mogeny difference features to localize the tampered image. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  23. [23]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Hao, J., Zhang, Z., Yang, S., Xie, D., Pu, S.: Transforensics: image forgery local- ization with dense self-attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15055–15064 (2021)

    2021

  24. [24]

    He,K.,Zhang,X.,Ren,S.,Sun,J.:Deepresiduallearningforimagerecognition.In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

    2016

  25. [25]

    Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and cameracharacteristicsconsistency.In: International Conferenceon Multimediaand Expo (2006)

    2006

  26. [26]

    ICLR1(2), 3 (2022)

    Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.: Lora: Low-rank adaptation of large language models. ICLR1(2), 3 (2022)

    2022

  27. [27]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)

    2018

  28. [28]

    In: Computer Vision–ECCV2020:16thEuropeanConference,Glasgow,UK,August23–28,2020, Proceedings, Part XXI 16

    Hu, X., Zhang, Z., Jiang, Z., Chaudhuri, S., Yang, Z., Nevatia, R.: Span: Spa- tial pyramid attention network for image manipulation localization. In: Computer Vision–ECCV2020:16thEuropeanConference,Glasgow,UK,August23–28,2020, Proceedings, Part XXI 16. pp. 312–328. Springer (2020)

    2020

  29. [29]

    IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

    Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., Chen, S.: Diffusion model-based image editing: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

    2025

  30. [30]

    In: Proceedings of the European conference on computer vision (ECCV)

    Huh, M., Liu, A., Owens, A., Efros, A.A.: Fighting fake news: Image splice detec- tion via learned self-consistency. In: Proceedings of the European conference on computer vision (ECCV). pp. 101–117 (2018) SARIF: Segment Anything for Robust Image Forensics 17

    2018

  31. [31]

    Nature394(6695), 784–787 (1998)

    Hupé,J.,James,A.,Payne,B.,Lomber,S.,Girard,P.,Bullier,J.:Corticalfeedback improves discrimination between figure and background by v1, v2 and v3 neurons. Nature394(6695), 784–787 (1998)

    1998

  32. [32]

    Psycho- logical review64(6p1), 384 (1957)

    Hurvich, L.M., Jameson, D.: An opponent-process theory of color vision. Psycho- logical review64(6p1), 384 (1957)

    1957

  33. [33]

    Advances in Neural Information Processing Systems36, 69625–69637 (2023)

    Jain, Y., Behl, H., Kira, Z., Vineet, V.: Damex: Dataset-aware mixture-of-experts for visual understanding of mixture-of-datasets. Advances in Neural Information Processing Systems36, 69625–69637 (2023)

    2023

  34. [34]

    In: Proceedings of the 7th workshop on Multimedia and security

    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th workshop on Multimedia and security. pp. 1–10 (2005)

    2005

  35. [35]

    Data6(10), 102 (2021)

    Kadam, K.D., Ahirrao, S., Kotecha, K.: Multiple image splicing dataset (misd): a dataset for multiple splicing. Data6(10), 102 (2021)

    2021

  36. [36]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., Irani, M.: Imagic: Text-based real image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6007–6017 (2023)

    2023

  37. [37]

    Advances in Neural Information Processing Systems36, 29914–29934 (2023)

    Ke, L., Ye, M., Danelljan, M., Tai, Y.W., Tang, C.K., Yu, F., et al.: Segment anything in high quality. Advances in Neural Information Processing Systems36, 29914–29934 (2023)

    2023

  38. [38]

    ACM Transactions on Graphics33(5), 1–21 (2014)

    Kee, E., O’brien, J.F., Farid, H.: Exposing photo manipulation from shading and shadows. ACM Transactions on Graphics33(5), 1–21 (2014)

    2014

  39. [39]

    Adam: A Method for Stochastic Optimization

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  40. [40]

    Science advances7(27), eabe1833 (2021)

    Kirchberger, L., Mukherjee, S., Schnabel, U.H., van Beest, E.H., Barsegyan, A., Levelt, C.N., Heimel, J.A., Lorteije, J.A., van der Togt, C., Self, M.W., et al.: The essential role of recurrent processing for figure-ground perception in mice. Science advances7(27), eabe1833 (2021)

    2021

  41. [41]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4015–4026 (2023)

    2023

  42. [42]

    IEEE Transactions on Information Forensics and Security12(4), 809–824 (2016)

    Korus, P., Huang, J.: Multi-scale analysis strategies in prnu-based tampering local- ization. IEEE Transactions on Information Forensics and Security12(4), 809–824 (2016)

    2016

  43. [43]

    Journal of neurophysiology16(1), 37–68 (1953)

    Kuffler,S.W.:Dischargepatternsandfunctionalorganizationofmammalianretina. Journal of neurophysiology16(1), 37–68 (1953)

    1953

  44. [44]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Kwon, M.J., Lee, W., Nam, S.H., Son, M., Kim, C.: Safire: Segment any forged image region. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 39, pp. 4437–4445 (2025)

    2025

  45. [45]

    Trends in neurosciences23(11), 571–579 (2000)

    Lamme, V.A., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends in neurosciences23(11), 571–579 (2000)

    2000

  46. [46]

    arXiv preprint arXiv:2412.15798 (2024)

    Lee, H., Kang, M., Han, B.: Diffusion-based conditional image editing through optimized inference with guidance. arXiv preprint arXiv:2412.15798 (2024)

    work page arXiv 2024

  47. [47]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Liu, C., Li, X., Ding, H.: Referring image editing: Object-level image editing via referring expressions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13128–13138 (2024)

    2024

  48. [48]

    IEEE Transactions on Circuits and Systems for Video Technology32(11), 7505–7517 (2022)

    Liu, X., Liu, Y., Chen, J., Liu, X.: Pscc-net: Progressive spatio-channel correlation network for image manipulation detection and localization. IEEE Transactions on Circuits and Systems for Video Technology32(11), 7505–7517 (2022)

    2022

  49. [49]

    SGDR: Stochastic Gradient Descent with Warm Restarts

    Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016) 18 D.-H. Moon, J.-H. Nam, and S.-C. Lee

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  50. [50]

    In: Security, steganography, and watermarking of multimedia contents VIII

    Lukáš, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sen- sor pattern noise. In: Security, steganography, and watermarking of multimedia contents VIII. vol. 6072, pp. 362–372. SPIE (2006)

    2006

  51. [51]

    Nature Communications15(1), 654 (2024)

    Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications15(1), 654 (2024)

    2024

  52. [52]

    Mareen, H., Karageorgiou, D., Wallendael, G.V., Lambert, P., Papadopoulos, S.: Tgif: Text-guided inpainting forgery dataset (2024).https://doi.org/10.48550/ arXiv.2407.11566, accepted at IEEE WIFS 2024

    work page arXiv 2024

  53. [53]

    Journal of Neuroscience24(15), 3736–3745 (2004)

    McMahon, M.J., Packer, O.S., Dacey, D.M.: The classical receptive field surround of primate parasol ganglion cells is mediated primarily by a non-gabaergic pathway. Journal of Neuroscience24(15), 3736–3745 (2004)

    2004

  54. [54]

    SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

    Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  55. [55]

    In: 2016 fourth international confer- ence on 3D vision (3DV)

    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international confer- ence on 3D vision (3DV). pp. 565–571. Ieee (2016)

    2016

  56. [56]

    Ieee Access11, 144497–144529 (2023)

    Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., Parkinson, S.: A survey on the detection and impacts of deepfakes in visual, audio, and textual formats. Ieee Access11, 144497–144529 (2023)

    2023

  57. [57]

    Neuro- computing p

    Nam, J.H., Kang, D.H., Lee, S.C.: M3fpolypsegnet++: Adaptive gaussian fre- quency decomposition with multi-task learning for polyp segmentation. Neuro- computing p. 131134 (2025)

    2025

  58. [58]

    Computer Vision and Image Understanding232, 103684 (2023)

    Nam, J.H., Lee, S.C.: Random image frequency aggregation dropout in image classification for deep convolutional neural networks. Computer Vision and Image Understanding232, 103684 (2023)

    2023

  59. [59]

    Pattern Recognition150, 110332 (2024)

    Nam, J.H., Lee, S.C.: Fsda: Frequency re-scaling in data augmentation for corruption-robust image classification. Pattern Recognition150, 110332 (2024)

    2024

  60. [60]

    Artificial Intelli- gence in Medicine p

    Nam, J.H., Lee, S.C.: Frequency-based boundary-guided attention network for do- main generalizable polyp segmentation from colonoscopy images. Artificial Intelli- gence in Medicine p. 103288 (2025)

    2025

  61. [61]

    arXiv preprint arXiv:2506.20922 (2025)

    Nam, J.H., Moon, D.H., Lee, S.C.: M2sformer: Multi-spectral and multi-scale at- tention with edge-aware difficulty guidance for image forgery localization. arXiv preprint arXiv:2506.20922 (2025)

    work page arXiv 2025

  62. [62]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Nam, J.H., Syazwany, N.S., Kim, S.J., Lee, S.C.: Modality-agnostic domain gen- eralizable medical image segmentation by multi-frequency in multi-scale atten- tion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11480–11491 (2024)

    2024

  63. [63]

    In: 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)

    Novozamsky, A., Mahdian, B., Saic, S.: Imd2020: A large-scale annotated dataset tailored for detecting manipulated images. In: 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW). pp. 71–80 (March 2020)

    2020

  64. [64]

    In: ACM SIGGRAPH 2023 conference proceedings

    Pan, X., Tewari, A., Leimkühler, T., Liu, L., Meka, A., Theobalt, C.: Drag your gan: Interactive point-based manipulation on the generative image manifold. In: ACM SIGGRAPH 2023 conference proceedings. pp. 1–11 (2023)

    2023

  65. [65]

    Symmetry11(1), 83 (2019)

    Pham, N.T., Lee, J.W., Kwon, G.R., Park, C.S.: Hybrid image-retrieval method for image-splicing validation. Symmetry11(1), 83 (2019)

    2019

  66. [66]

    In: International workshop on information hiding

    Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: International workshop on information hiding. pp. 128–147. Springer (2004)

    2004

  67. [67]

    IEEE Transactions on Signal Processing53(10), 3948–3959 (2005) SARIF: Segment Anything for Robust Image Forensics 19

    Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing53(10), 3948–3959 (2005) SARIF: Segment Anything for Robust Image Forensics 19

    2005

  68. [68]

    SAM 2: Segment Anything in Images and Videos

    Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., et al.: Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  69. [69]

    In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomed- ical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18. pp. 234–241. Springer (2015)

    2015

  70. [70]

    Autosam: Adapting sam to medical images by overloading the prompt encoder,

    Shaharabany, T., Dahan, A., Giryes, R., Wolf, L.: Autosam: Adapting sam to med- ical images by overloading the prompt encoder. arXiv preprint arXiv:2306.06370 (2023)

    work page arXiv 2023

  71. [71]

    Tahir, E., Bal, M.: Deep image composition meets image forgery (2024)

    2024

  72. [72]

    In: Proceedings ELMAR-2013

    Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: Comofod—new database for copy- move forgery detection. In: Proceedings ELMAR-2013. pp. 49–54. IEEE (2013)

    2013

  73. [73]

    Social media+ society6(1), 2056305120903408 (2020)

    Vaccari, C., Chadwick, A.: Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social media+ society6(1), 2056305120903408 (2020)

    2020

  74. [74]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Wang, J., Wu, Z., Chen, J., Han, X., Shrivastava, A., Lim, S.N., Jiang, Y.G.: Objectformer for image manipulation detection and localization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2364–2373 (2022)

    2022

  75. [75]

    Knowledge-Based Systems 314, 113159 (2025).https://doi.org/10.1016/j.knosys.2025.113159

    Wang, Y., Li, H., Hou, S., Dong, Z., Gao, R.: Distortion information and edge fea- tures guided network for real-world image restoration. Knowledge-Based Systems 314, 113159 (2025).https://doi.org/10.1016/j.knosys.2025.113159

    work page doi:10.1016/j.knosys.2025.113159 2025

  76. [76]

    In: Proceedings of the European conference on computer vision (ECCV)

    Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)

    2018

  77. [77]

    Wu, J., Ji, W., Liu, Y., Fu, H., Xu, M., Xu, Y., Jin, Y.: Medical sam adapter: Adapting segment anything model for medical image segmentation (2023)

    2023

  78. [78]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition

    Wu, Y., AbdAlmageed, W., Natarajan, P.: Mantra-net: Manipulation tracing net- work for detection and localization of image forgeries with anomalous features. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition. pp. 9543–9552 (2019)

    2019

  79. [79]

    IEEE Transactions on Artificial Intelligence (2024)

    Xu, X., Chen, J., Lv, W., Wang, W., Zhang, Y.: Image tampering detection with frequency-aware attention and multi-view fusion. IEEE Transactions on Artificial Intelligence (2024)

    2024

  80. [80]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., Wen, F.: Paint by example: Exemplar-based image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18381–18391 (2023)

    2023

Showing first 80 references.