Area of research
Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Artificial intelligence, Digital watermarking, Data compression, Robustness (evolution), and Computer vision.
Inter-frame residual frequency-based reconstruction learning for deep video frame interpolation detection
A novel robust black-box fingerprinting scheme for deep classification neural networks
A Unique Identification-Oriented Black-Box Watermarking Scheme for Deep Classification Neural Networks
Exposing low-quality deepfake videos of Social Network Service using Spatial Restored Detection Framework
A two-stage robust reversible watermarking using polar harmonic transform for high robustness and capacity
A Customized Deep Network Based Encryption-Then-Lossy-Compression Scheme of Color Images Achieving Arbitrary Compression Ratios
A Two-Stage Robust Reversible Watermarking Using Polar Harmonic Transform for High Robustness and Capacity
Enhancing Performance of Lossy Compression on Encrypted Gray Images through Heuristic Optimization of Bitplane Allocation
Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number
Exposing Video Compression History by Detecting Transcoded HEVC Videos from AVC Coding
Exposing Video Forgeries by Detecting Misaligned Double Compression
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation