论文标题
具有自适应参数激活的图像的切解分析
Steganalysis of Image with Adaptively Parametric Activation
论文作者
论文摘要
斯坦分析是一种检测图像是否包含Se-cret信息的方法，是一项至关重要的研究，避免了侵蚀性造影的危害。切解分析的目的是检测弱嵌入信号，这些信号几乎无法通过卷积 - 层学习并易于抑制。在本文中，为了增强嵌入式信号，我们从减少嵌入信号丢失和增强嵌入信号捕获能力的方面研究了激活函数，过滤器和损耗函数的不足。 Adap-Tive参数激活模块旨在保留Nega-Tive嵌入信号。为了嵌入信号捕获能力增强功能，我们在高通滤波器上增加了限制，以使剩余多样性使过滤器提取丰富的嵌入信号。此外，采用基于对比度学习的损失函数来克服最大阶层距离的跨透明损失的局限性。它有助于网络区分嵌入信号和语义边缘。我们使用Bossbase 1.01的图像，并制作WOW和S-Uniward的Stegos进行实验。与最先进的方法相比，我们的方法具有竞争性能。
Steganalysis as a method to detect whether image contains se-cret message, is a crucial study avoiding the imperils from abus-ing steganography. The point of steganalysis is to detect the weak embedding signals which is hardly learned by convolution-al layer and easily suppressed. In this paper, to enhance embed-ding signals, we study the insufficiencies of activation function, filters and loss function from the aspects of reduce embedding signal loss and enhance embedding signal capture ability. Adap-tive Parametric Activation Module is designed to reserve nega-tive embedding signal. For embedding signal capture ability enhancement, we add constraints on the high-pass filters to im-prove residual diversity which enables the filters extracts rich embedding signals. Besides, a loss function based on contrastive learning is applied to overcome the limitations of cross-entropy loss by maximum inter-class distance. It helps the network make a distinction between embedding signals and semantic edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD for experiments. Compared to state-of-the-art methods, our method has a competitive performance.
