The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often present conflicting goals that classical methods have struggled to reconcile. This paper extends and enhances an existing image-in-audio deep steganography method by focusing on improving its robustness. The proposed enhancements include modifications to the loss function, utilization of the Short-Time Fourier Transform (STFT), introduction of redundancy in the encoding process for error correction, and buffering of additional information in the pixel subconvolution operation. The results demonstrate that our approach outperforms the existing method in terms of robustness and perceptual transparency.

翻译：由于最近AI动力技术的进步,特别是在能够将信号隐藏在不同性质的信号范围内的多式设置方面,对摄像学领域的兴趣大增。所有摄像学方法的主要目标是实现感知透明、稳健和大量嵌入能力,这往往带来传统方法难以调和的相互冲突的目标。本文件通过侧重于提高它的稳健性来扩展和加强现有的视像内深色色方法。拟议的改进包括修改损失功能、利用短时四轮变(STFT),在编码过程中引入重复错误纠正,以及在像素次演进操作中缓冲补充信息。结果显示,我们的方法在稳健和感性透明方面超过了现有的方法。</s>