Yuan Rao

Orcid: 0000-0002-3150-5103

Affiliations:
  • Guangzhou University, Institute of Artificial Intelligence, Guangzhou, China
  • Sun Yat-sen University (SYSU), School of Electronics and Information Technology, Guangzhou, China (PhD 2021)


According to our database1, Yuan Rao authored at least 12 papers between 2016 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Reversible generative steganography with distribution-preserving.
Cybersecur., December, 2025

Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2025

A trigger-perceivable backdoor attack framework driven by image steganography.
Pattern Recognit., 2025

2024
Exploring the vulnerability of self-supervised monocular depth estimation models.
Inf. Sci., 2024

Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography.
Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

2023
Deep Multi-image Hiding with Random Key.
Proceedings of the Artificial Intelligence Security and Privacy, 2023

MKD: Mutual Knowledge Distillation for Membership Privacy Protection.
Proceedings of the Artificial Intelligence Security and Privacy, 2023

2021
Multi-semantic CRF-based attention model for image forgery detection and localization.
Signal Process., 2021

Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Deep Learning Local Descriptor for Image Splicing Detection and Localization.
IEEE Access, 2020

2017
Block-Based Convolutional Neural Network for Image Forgery Detection.
Proceedings of the Digital Forensics and Watermarking - 16th International Workshop, 2017

2016
A deep learning approach to detection of splicing and copy-move forgeries in images.
Proceedings of the IEEE International Workshop on Information Forensics and Security, 2016


  Loading...