The act of secretly embedding and extracting a watermark on a cover image to protect it is
known as image watermarking. In recent years, deep learning-based image watermarking …

This paper presents a comprehensive survey of deep learning-based image watermarking;
this technique entails the invisible embedding and extraction of watermarks within a cover …

The unprecedented success of deep learning could not be achieved without the synergy of
big data, computing power, and human knowledge, among which none is free. This calls for …

The vulnerability of deep learning models to adversarial attacks is a growing concern, as the
emergence of adversarial samples exposes almost all models to the risk of such attacks …

Medical images on the Internet of Medical Things (IoMT) can be easily collected, recognized
and analyzed by unauthorized individuals and companies using deep neural networks …

Making adversarial samples to fool deep neural network (DNN) is an emerging research
direction of privacy protection, since the output of the attacker's DNN can be easily changed …

In recent studies of generative adversarial networks (GAN), researchers have attempted to
combine adversarial perturbation with data hiding in order to protect the privacy and …

In recent years, the uploading of massive personal images has increased the security risks,
mainly including privacy breaches and copyright infringement. Adversarial examples …

Digital image watermarking used to be an important tool for copyright protection. However,
as neural network-based watermark removal methods have been proposed in recent years …

身份证认证场景多采用文本识别模型对身份证图片的字段进行提取, 识别和身份认证,
存在很大的隐私泄露隐患. 并且, 当前基于文本识别模型的对抗攻击算法大多只考虑简单背景的 …