4 citations · 14 across the 9 of their papers we have counts for
10 papers
Forensic-Aware Continual Adaptation for Image Forgery Localization
Chenqi Kong, Song Xia, Anwei Luo +3
The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods can accurately localize manipula…
ActivityForensics: A Comprehensive Benchmark for Localizing Manipulated Activity in Videos
Peijun Bao, Anwei Luo, Gang Pan +2
Temporal forgery localization aims to temporally identify manipulated segments in videos. Most existing benchmarks focus on appearance-level forgeries, such as face swapping and ob…
Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance
Haipeng Li, Rongxuan Peng, Anwei Luo +3
The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing evaluation protocols largely overlook…
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…
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks
Jin Li, Ziqiang He, Anwei Luo +3
Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by int…
Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
Chenqi Kong, Anwei Luo, Peijun Bao +5
Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations…
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Chenqi Kong, Anwei Luo, Peijun Bao +5
Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expr…
Pixel-Inconsistency Modeling for Image Manipulation Localization
Chenqi Kong, Anwei Luo, Shiqi Wang +3
Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localizat…
Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer
Anwei Luo, Rizhao Cai, Chenqi Kong +4
With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains…
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection
Anwei Luo, Chenqi Kong, Jiwu Huang +3
Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as n…