1 citations · 1 across the 16 of their papers we have counts for
18 papers
Prompt-Segmentation Meets ROI Encryption: Fine-Grained Video Protection Based on Diffusion Isolation
Xiang Zhang, Haoyan Lu, Ziqiang Li +4
Region-of-interest (ROI) selective encryption in H.265/HEVC aims to protect sensitive content while preserving the utility of non-ROI regions. Existing coding-unit (CU)-level appro…
Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration
Jun Li, Lizhi Xiong, Ziqiang Li +4
Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in larg…
Scaling Exposes the Trigger: Input-Level Backdoor Detection in Text-to-Image Diffusion Models via Cross-Attention Scaling
Zida Li, Jun Li, Yuzhe Sha +3
Text-to-image (T2I) diffusion models have achieved remarkable success in image synthesis, but their reliance on large-scale data and open ecosystems introduces serious backdoor sec…
Semantic Manipulation Localization
Zhenshan Tan, Chenhan Lu, Yuxiang Huang +6
Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and generative models, many manipulation…
A H.265/HEVC Fine-Grained ROI Video Encryption Algorithm Based on Coding Unit and Prompt Segmentation
Xiang Zhang, Haoyan Lu, Ziqiang Li +4
ROI (Region of Interest) video selective encryption based on H.265/HEVC is a technology that protects the sensitive regions of videos by perturbing the syntax elements associated w…
A Video Steganography for H.265/HEVC Based on Multiple CU Size and Block Structure Distortion
Xiang Zhang, Wen Jiang, Fei Peng +3
Video steganography based on block structure, which embeds secret information by modifying Coding Unit (CU) block structure of I-frames, is currently a research hotspot. However, t…
H.265/HEVC Video Steganalysis Based on CU Block Structure Gradients and IPM Mapping
Xiang Zhang, Haiyang Xia, Fan Wang +4
Existing H.265/HEVC video steganalysis research mainly focuses on detecting the steganography based on motion vectors, intra prediction modes, and transform coefficients. However,…
Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework
Xinjue Hu, Chi Wang, Boyu Wang +3
Deep image steganography (DIS) has achieved significant results in capacity and invisibility. However, current paradigms enforce the secret image to maintain the same resolution as…
Low-rank Orthogonal Subspace Intervention for Generalizable Face Forgery Detection
Chi Wang, Xinjue Hu, Boyu Wang +2
The generalization problem remains a key challenge in face forgery detection. This paper explores the reasons for the generalization failure of Vanilla CLIP: in ``real vs. fake" de…
Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition
Xiang Zhang, Wenliang Weng, Daoyong Fu +4
Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises because artifact patterns vary…
A Visual Perception-Based Tunable Framework and Evaluation Benchmark for H.265/HEVC ROI Encryption
Xiang Zhang, Geng Wu, Wenbin Huang +3
ROI selective encryption, as an efficient privacy protection technique, encrypts only the key regions in the video, thereby ensuring security while minimizing the impact on coding…
How Noise Benefits AI-generated Image Detection
Ziqiang Li, Jiazhen Yan, Fan Wang +2
The rapid advancement of generative models has made real and synthetic images increasingly indistinguishable. Although extensive efforts have been devoted to detecting AI-generated…
DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection
Jiazhen Yan, Ziqiang Li, Fan Wang +3
The rapid progress of generative models such as GANs and diffusion models has led to the widespread proliferation of AI-generated images, raising concerns about misinformation, pri…
NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection
Jiazhen Yan, Fan Wang, Weiwei Jiang +3
The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing concerns over their misuse in s…
Is Artificial Intelligence Generated Image Detection a Solved Problem?
Ziqiang Li, Jiazhen Yan, Ziwen He +4
The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinfo…
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
Ziqiang Li, Jun Li, Lizhi Xiong +2
Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals…
Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection
Jiazhen Yan, Ziqiang Li, Fan Wang +2
The rapid advancement of Generative Adversarial Networks (GANs) and diffusion models has enabled the creation of highly realistic synthetic images, presenting significant societal…
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks
Ziqiang Li, Yueqi Zeng, Pengfei Xia +3
With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common fo…