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Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary
Hence, this study aims to look at the AEs from a different perspective and proposes an imperceptible adversarial attack that systemically attacks the input image boundary for finding the AEs.
A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking
This paper presents a comprehensive survey on deep learning-based image watermarking, a technique that entails the invisible embedding and extraction of watermarks within a cover image, aiming to offer a seamless blend of robustness and adaptability.
Padding Module: Learning the Padding in Deep Neural Networks
During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation.
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