18 citations · 20 across the 11 of their papers we have counts for
12 papers
PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection
Xiaoyu Zhou, Jianwei Fei, Peipeng Yu +3
The rapid evolution of generative AI, from GANs to modern diffusion models, has resulted in increasingly subtle discriminative clues. These fine-grained signals are often overshado…
ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation
Junyan Luo, Peipeng Yu, Jianwei Fei +4
Deepfake technologies have rapidly advanced with modern generative AI, and face swapping in particular poses serious threats to privacy and digital security. Existing proactive def…
Dual-Guard: Dual-Channel Latent Watermarking for Provenance and Tamper Localization in Diffusion Images
JinFeng Xie, Chengfu Ou, Peipeng Yu +5
The rapid adoption of diffusion-based generative models has intensified concerns over the attribution and integrity of AI-generated content (AIGC). Existing single-domain watermark…
LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction
Zixuan Shen, Zhihua Xia, Kaikai Gan +1
In face recognition systems, facial templates are widely adopted for identity authentication due to their compliance with the data minimization principle. However, facial template…
High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking
Peipeng Yu, Jinfeng Xie, Chengfu Ou +5
The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection. Existing versatile watermarking syst…
C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs
Xuan Feng, Bo An, Tianlong Gu +4
Bias in Large Language Models (LLMs) poses significant risks to trustworthiness, manifesting primarily as stereotypical biases (e.g., gender or racial stereotypes) and structural b…
CLIP-FTI: Fine-Grained Face Template Inversion via CLIP-Driven Attribute Conditioning
Longchen Dai, Zixuan Shen, Zhiheng Zhou +2
Face recognition systems store face templates for efficient matching. Once leaked, these templates pose a threat: inverting them can yield photorealistic surrogates that compromise…
Fine-Grained DINO Tuning with Dual Supervision for Face Forgery Detection
Tianxiang Zhang, Peipeng Yu, Zhihua Xia +3
The proliferation of sophisticated deepfakes poses significant threats to information integrity. While DINOv2 shows promise for detection, existing fine-tuning approaches treat it…
Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection
Peipeng Yu, Jianwei Fei, Hui Gao +3
Current Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake dete…
DFREC: DeepFake Identity Recovery Based on Identity-aware Masked Autoencoder
Peipeng Yu, Hui Gao, Jianwei Fei +3
Recent advances in deepfake forensics have primarily focused on improving the classification accuracy and generalization performance. Despite enormous progress in detection accurac…
CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts
Peipeng Yu, Jiahan Chen, Xuan Feng +1
The powerful ability of ChatGPT has caused widespread concern in the academic community. Malicious users could synthesize dummy academic content through ChatGPT, which is extremely…
Learning Second Order Local Anomaly for General Face Forgery Detection
Jianwei Fei, Yunshu Dai, Peipeng Yu +3
In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused…