Recently, convolutional neural networks (CNNs) have demonstrated superior performance on digital multimedia steganalysis. However, some studies have noted that most CNN-based classifiers can be easily fooled by adversarial examples, which form slightly perturbed inputs to a target network according to the gradients. Inspired by this phenomenon, we first introduce a novel steganography method based on adversarial examples for digital audio in the time domain. Unlike related methods for image steganography, such as [1]-[4], which are highly dependent on some existing embedding costs, the proposed method can start from a flat or even a random embedding cost and then iteratively update the initial costs by exploiting the adversarial attacks until satisfactory security performances are obtained. The extensive experimental results show that our method significantly outperforms the existing nonadaptive and adaptive steganography methods and achieves state-of-the-art results. Moreover, we also provide experimental results to investigate why the proposed embedding modifications seem evenly located at all audio segments despite their different content complexities, which is contrary to the content adaptive principle widely employed in modern steganography methods.

中文翻译：

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基于针对卷积神经网络的迭代对抗攻击的音频隐写术


最近，卷积神经网络（CNN）在数字多媒体隐写分析方面表现出了卓越的性能。然而，一些研究指出，大多数基于 CNN 的分类器很容易被对抗性示例所欺骗，这些示例根据梯度形成对目标网络的轻微扰动的输入。受这种现象的启发，我们首先介绍一种基于时域数字音频对抗示例的新型隐写术方法。与图像隐写术的相关方法（例如[1]-[4]）不同，这些方法高度依赖于一些现有的嵌入成本，所提出的方法可以从平坦甚至随机的嵌入成本开始，然后通过利用迭代更新初始成本对抗性攻击，直到获得令人满意的安全性能。大量的实验结果表明，我们的方法显着优于现有的非自适应和自适应隐写术方法，并取得了最先进的结果。此外，我们还提供了实验结果来调查为什么所提出的嵌入修改似乎均匀地位于所有音频片段，尽管它们的内容复杂性不同，这与现代隐写术方法中广泛采用的内容自适应原理相反。