activity
20232026
most citedFSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning

10 citations · 15 across the 18 of their papers we have counts for

collaborators

19 papers

cs.SD2026

Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection

Miao He, Peng Cheng, Zhongjie Ba +4

Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve s…

cs.CV2026

Ghosts Beneath Textures: Texture-Relation Cues for Cross-Paradigm AI-Generated Image Detection

Haoyu Wang, Yiming Qin, Zhongjie Ba +4

AI-generated images have proliferated rapidly, motivating extensive research. Most existing AI-generated image detectors are developed and evaluated under image-free generation par…

cs.CR2026

"Training robust watermarking model may hurt authentication!'' Exploring and Mitigating the Identity Leakage in Robust Watermarking

Xinyu Zhang, Ziping Dong, Qingyu Liu +3

The rapid advancement of generative AI has underscored the critical need for identifying image ownership and protecting copyrights. This makes post-processing image watermarking an…

cs.AI2026★ 1 cited

When AI reviews science: Can we trust the referee?

Jialiang Wang, Yuchen Liu, Hang Xu +7

The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large langu…

cs.CV2026

When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection

Chao Shuai, Shaojing Fan, Chenlin Zou +6

The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-generated image detection. Althou…

cs.SD2026

HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection

Qing Wen, Haohao Li, Zhongjie Ba +4

Advances in AIGC technologies have enabled the synthesis of highly realistic audio deepfakes capable of deceiving human auditory perception. Although numerous audio deepfake detect…

cs.CV2025

Scalable Face Security Vision Foundation Model for Deepfake, Diffusion, and Spoofing Detection

Gaojian Wang, Feng Lin, Tong Wu +2

With abundant, unlabeled real faces, how can we learn robust and transferable facial representations to boost generalization across various face security tasks? We make the first a…

cs.CV2025

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization

Chao Shuai, Gaojian Wang, Kun Pan +7

While the pursuit of higher accuracy in deepfake detection remains a central goal, there is an increasing demand for precise localization of manipulated regions. Despite the remark…

cs.CR2025

Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models

Mingjie Chen, Tiancheng Zhu, Mingxue Zhang +4

Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against…

cs.CV2025

Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models

Zhenguang Liu, Chao Shuai, Shaojing Fan +4

Diffusion models have achieved remarkable success in novel view synthesis, but their reliance on large, diverse, and often untraceable Web datasets has raised pressing concerns abo…

cs.CR2025

WMCopier: Forging Invisible Image Watermarks on Arbitrary Images

Ziping Dong, Chao Shuai, Zhongjie Ba +4

Invisible Image Watermarking is crucial for ensuring content provenance and accountability in generative AI. While Gen-AI providers are increasingly integrating invisible watermark…

cs.CV2025

Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation

Zhongjie Ba, Yitao Zhang, Peng Cheng +4

Watermarking plays a key role in the provenance and detection of AI-generated content. While existing methods prioritize robustness against real-world distortions (e.g., JPEG compr…

cs.CV2025

Robust Representation Consistency Model via Contrastive Denoising

Jiachen Lei, Julius Berner, Jiongxiao Wang +5

Robustness is essential for deep neural networks, especially in security-sensitive applications. To this end, randomized smoothing provides theoretical guarantees for certifying ro…

cs.CV2024★ 10 cited

FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning

Gaojian Wang, Feng Lin, Tong Wu +3

This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks with respect to generaliza…

cs.CR2024★ 2 cited

Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization

Tiantian Liu, Hongwei Yao, Feng Lin +3

Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations tha…

cs.CR2024

ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features

Peng Cheng, Yuwei Wang, Peng Huang +5

Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial att…

cs.CR2024★ 2 cited

Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning

Binhao Ma, Tianhang Zheng, Hongsheng Hu +5

Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with…

cs.CV2024

Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection

Zhongjie Ba, Qingyu Liu, Zhenguang Liu +4

Deepfake technology has given rise to a spectrum of novel and compelling applications. Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive…

cs.CV2023

GLOW: Global Layout Aware Attacks on Object Detection

Buyu Liu, BaoJun, Jianping Fan +3

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural…