In this letter, a lightweight and effective deep steganalysis network (DSN) with less than 400,000 parameters, called LWENet, is proposed, which focuses on increasing the performance as well as significantly reducing the number of parameters (NP) from three perspectives. Firstly, in the preprocessing part, several lightweight bottleneck residual blocks are combined into the spatial rich model filters to improve the signal-to-noise ratio of stego signals while slightly increasing NP, thereby improving the subsequent performance. Secondly, a depthwise separable convolution layer is exploited at the end of the feature extraction part to largely reduce NP and increase the performance by capturing salient correlations while ignoring trivial ones among feature maps. Finally, to keep LWENet lightweight, we have to select only one fully connected (FC) layer. Simultaneously, multi-view global pooling is employed prior to the FC layer to yield multi-view features and further improve the detection performance. Extensive experiments demonstrate that our network achieves better performance than several state-of-the-art DSNs.

中文翻译：

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轻量级有效的深度图像隐写分析网络

在这封信中，提出了一种轻量级且有效的深度隐写分析网络（DSN），其参数少于 400,000 个，称为 LWENet，其重点是从三个角度提高性能并显着减少参数数量（NP）。首先，在预处理部分，将几个轻量级的瓶颈残差块组合到空间丰富的模型滤波器中，以提高隐秘信号的信噪比，同时略微增加 NP，从而提高后续性能。其次，在特征提取部分的末尾利用深度可分离卷积层通过捕获显着相关性同时忽略特征图中的琐碎相关性来大大降低 NP 并提高性能。最后，为了保持 LWENet 的轻量级，我们只需要选择一个全连接（FC）层。同时，在FC层之前采用多视图全局池化来产生多视图特征并进一步提高检测性能。大量实验表明，我们的网络比几个最先进的 DSN 实现了更好的性能。