为了缩小当前无限制攻击与理想无限制攻击的差距,作者提出了一种无限制攻击框架——Content-based Unre- stricted Adversarial Attack 。首先将图像映射到低维流形上,这个低维流形由生成模型表示,并表示为潜在空间;随后沿着低维流形可以生成更加多样化的图像,优化这个潜在空间的对抗目标可以生成更加多样化的对抗内容。 文章...
Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photorealistic, demonstrating their ability to deceive human perception and deep neural networks with stealth and success. However, ...
Gaps in understanding and creativity between models and humans. By simulating human learning and cognitive processes, GAI models are trained based on extensive datasets and sophisticated algorithms. However, in real-world applications, they lack the equivalent capabilities as humans, such as understanding...
Conse- quently, the unrestricted generation ability of GAI models may yield inappropriate content. • Inadequacies in model training datasets. TTI mod- els are trained on large, diverse datasets and have the ability to generate images from textual descriptions. However, these datasets often ...
By analyzing the structure and loading mechanism of portable executable (PE) files, this paper proposes an unrestricted add-amount bytecode attack (BAUAA). BAUAA generates adversarial samples by adding bytecode to a "section additional space" in the PE file that is scattered after each section ...