A5067350641- Advanced Steganography and Watermarking Techniques
- Chaos-based Image/Signal Encryption
- Digital Media Forensic Detection
- Face and Expression Recognition
- Cellular Automata and Applications
University of Macau
2024
Shenzhen Institutes of Advanced Technology
2021-2022
University of Chinese Academy of Sciences
2022
Chinese Academy of Sciences
2021-2022
Spread spectrum (SS) watermarking has gained significant attention as it prevents attackers from reading, tampering with, or removing watermarks. Secret key estimation can help with the first two unauthorized operations but cannot remove Moreover, existing deep-learning watermark removal methods do not consider characteristics of SS watermarking, thus leading to unsatisfactory results. In this paper, we design a secret method that treats binary classification problem and updates estimated...
Over the past two decades, spread spectrum (SS) embedding has been widely used in digital watermarking due to its competitive performance robustness and security. However, of existing secure SS methods, such as natural (NW) robust-NW (RNW), is still weak. In this article, we propose a new method named truncated-RNW (TRNW), which improves RNW while maintaining same security level. The main idea TRNW move RNW-watermarked correlations within an origin-centered sphere onto spherical surface...
The security of spread spectrum (SS) watermarking largely depends on the difficulty estimating its secret key. Some estimators have been proposed to estimate key in known-message attack (KMA) scenario. However, estimation accuracies existing are not satisfactory when number observations is large enough. Currently, it still a challenging and open problem design more effective estimators. In this paper, we propose an equivalent keys (EK)-based estimator for both traditional secure SS methods....
Subspace learning has been widely applied for joint feature extraction and dimensionality reduction, demonstrating significant efficacy. Numerous subspace methods with diverse assumptions regarding the criteria target subspaces have developed to obtain compact interpretable data representations. However, when image data, existing fail fully exploit inherent correlations within set. This paper proposes a Robust Discriminative t-Linear Learning model (RDtSL) tackle this issue using t-product....