Search Results for author:
Found 7 papers, 6 papers with code
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence.
Towards an Accurate and Secure Detector against Adversarial Perturbations
It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.
Representing Noisy Image Without Denoising
In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.
Detecting Recolored Image by Spatial Correlation
In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
A Principled Design of Image Representation: Towards Forensic Tasks
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation
Image representation is an important topic in computer vision and pattern recognition.
