290 citations · 1k across the 19 of their papers we have counts for
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SDiFL: Stable Diffusion-Driven Framework for Image Forgery Localization
Yang Su, Shunquan Tan, Jiwu Huang
Driven by the new generation of multi-modal large models, such as Stable Diffusion (SD), image manipulation technologies have advanced rapidly, posing significant challenges to ima…
ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack
Rongxuan Peng, Shunquan Tan, Chenqi Kong +3
Parameter-efficient fine-tuning (PEFT) has emerged as a popular strategy for adapting large vision foundation models, such as the Segment Anything Model (SAM) and LLaVA, to downstr…
CLUE: Leveraging Low-Rank Adaptation to Capture Latent Uncovered Evidence for Image Forgery Localization
Youqi Wang, Shunquan Tan, Rongxuan Peng +2
The increasing accessibility of image editing tools and generative AI has led to a proliferation of visually convincing forgeries, compromising the authenticity of digital media. I…
Active Adversarial Noise Suppression for Image Forgery Localization
Rongxuan Peng, Shunquan Tan, Xianbo Mo +2
Recent advances in deep learning have significantly propelled the development of image forgery localization. However, existing models remain highly vulnerable to adversarial attack…