48 citations · 60 across the 30 of their papers we have counts for
13 papers · 1 filter
Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model
Decheng Liu, Xijun Wang, Chunlei Peng +3
Adversarial attacks involve adding perturbations to the source image to cause misclassification by the target model, which demonstrates the potential of attacking face recognition…
Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis
Xingyilang Yin, Xi Yang, Liangchen Liu +2
Recently MLP-based methods have shown strong performance in point cloud analysis. Simple MLP architectures are able to learn geometric features in local point groups yet fail to mo…
Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image Retrieval
Decheng Liu, Xu Luo, Chunlei Peng +3
This paper studies the problem of zero-shot sketch-based image retrieval (ZS-SBIR), which aims to use sketches from unseen categories as queries to match the images of the same cat…
Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking
Yan Gao, Haojun Xu, Nannan Wang +2
The global multi-object tracking (MOT) system can consider interaction, occlusion, and other ``visual blur'' scenarios to ensure effective object tracking in long videos. Among the…
DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
Chunlei Peng, Huiqing Guo, Decheng Liu +3
Deepfake detection refers to detecting artificially generated or edited faces in images or videos, which plays an essential role in visual information security. Despite promising p…
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model
Guozhang Li, Xinpeng Ding, De Cheng +3
Early weakly supervised video grounding (WSVG) methods often struggle with incomplete boundary detection due to the absence of temporal boundary annotations. To bridge the gap betw…
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization
Ruoyu Zhao, Mingrui Zhu, Shiyin Dong +2
We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images…
GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning
Qinlin He, Chunlei Peng, Decheng Liu +2
DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming incre…
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria
Nuoyan Zhou, Nannan Wang, Decheng Liu +2
Deep neural networks are vulnerable to adversarial noise. Adversarial Training (AT) has been demonstrated to be the most effective defense strategy to protect neural networks from…
Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification
Mingrui Zhu, Dongxin Chen, Xin Wei +2
In an era where personal photos are easily leaked and collected, face de-identification is a crucial method for protecting identity privacy. However, current face de-identification…
Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection
Decheng Liu, Tao Chen, Chunlei Peng +3
Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forg…
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow
Lin Yuan, Kai Liang, Xiao Pu +5
This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single…
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
Yulong Yang, Chenhao Lin, Qian Li +6
Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against r…