arXiv:2211.14079
Training Data Improvement for Image Forgery Detection using Comprint
원문 초록
Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation. Therefore, Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance. Most interestingly, we found that including images compressed with low quality factors during training does not have a significant effect on the accuracy, whereas incorporating recompression boosts the robustness. As such, consumers can use Comprint on their smartphones to verify the authenticity of images.
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Learning Universal Features for Generalizable Image Forgery Localization
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
Deep Image Restoration For Image Anti-Forensics
Deep Image Composition Meets Image Forgery
Fusion Transformer with Object Mask Guidance for Image Forgery Analysis
토론
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