Recent studies shows that the majority of existing deep steganalysis models have a large amount of redundancy, which leads to a huge waste of storage and computing resources. The existing model compression method cannot flexibly compress the convolutional layer in residual shortcut block so that a satisfactory shrinking rate cannot be obtained. In this paper, we propose STD-NET, an unsupervised deep-learning architecture search approach via hierarchical tensor decomposition for image steganalysis. Our proposed strategy will not be restricted by various residual connections, since this strategy does not change the number of input and output channels of the convolution block. We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one. Extensive experiments have confirmed that, on one hand, our model can achieve comparable or even better detection performance in various steganalytic scenarios due to the great adaptivity of the obtained network architecture. On the other hand, the experimental results also demonstrate that our proposed strategy is more efficient and can remove more redundancy compared with previous steganalytic network compression methods.
翻译:最近的研究显示,大多数现有的深层分解模型存在大量冗余,导致大量浪费储存和计算资源。现有的模型压缩方法无法灵活压缩剩余捷径区块中的卷变层,从而无法取得令人满意的收缩率。在本文中,我们提出STD-NET,这是一个未经监督的深层建筑搜索方法,通过高压分解等级分解法进行图像分解。我们的拟议战略不会受到各种剩余连接的限制,因为这一战略不会改变卷发区输入和输出渠道的数量。我们提议了一个正常的扭曲阈值,以评价基础模型中每个涉及的卷变层的敏感度,用以指导STD-NET以高效和不受监督的方式压缩目标网络,并获得两种不同形状的网络结构,其计算成本低,性能与最初的网络类似。广泛的实验证实,一方面,由于获得的网络结构的高度适应性能,我们的模型可以在各种分解情景中实现可比的甚至更好的检测性能。另一方面,实验结果还表明,与先前的战略相比,我们提议的压缩性能更高效地消除了以前的战略。