A deep learning approach to detection of splicing and copy-move forgeries in images. - Yuan Rao 002; Jiangqun Ni (WIFS, 2016)

Block-Based Convolutional Neural Network for Image Forgery Detection. - Jianghong Zhou; Jiangqun Ni; Yuan Rao 002 (IWDW, 2017)

Deep Learning Local Descriptor for Image Splicing Detection and Localization. - Yuan Rao 002; Jiangqun Ni; Huimin Zhao 001 (IEEE Access, 2020)

Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images. - Yuan Rao 002; Jiangqun Ni (ICCV, 2021)

Multi-semantic CRF-based attention model for image forgery detection and localization. - Yuan Rao 002; Jiangqun Ni; Hao Xie 002 (Signal Process., 2021)

MKD: Mutual Knowledge Distillation for Membership Privacy Protection. - Sihao Huang; Zhongxiang Liu; Jiafu Yu; Yongde Tang; Zidan Luo; Yuan Rao 002 (AIS&P (1), 2023)

Deep Multi-image Hiding with Random Key. - Wei Zhang 016; Weixuan Tang; Yuan Rao 002; Bin Li 011; Jiwu Huang (AIS&P (1), 2023)

Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography. - Weixuan Tang; Haoyu Yang; Yuan Rao 002; Zhili Zhou 001; Fei Peng 001 (ACM Multimedia, 2024)

Exploring the vulnerability of self-supervised monocular depth estimation models. - Ruitao Hou; Kanghua Mo; Yucheng Long; Ning Li; Yuan Rao 002 (Inf. Sci., 2024)

A trigger-perceivable backdoor attack framework driven by image steganography. - Weixuan Tang; Jiahao Li; Yuan Rao 002; Zhili Zhou 001; Fei Peng 001 (Pattern Recognit., 2025)

Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation. - Yuan Rao 002; Jiangqun Ni; Weizhe Zhang; Jiwu Huang (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Reversible generative steganography with distribution-preserving. - Weixuan Tang; Yuan Rao 002; Zuopeng Yang; Fei Peng 001; Xutong Cui; Junhao Huang; Peijun Zhu (Cybersecur., 2025)