The steganalysis of JPEG images is a crucial area of research. Deep-learning based steganalysis methods have achieved superior detection performance. All methods for JPEG steganalysis rely on residual networks. Although the incorporation of residual connections has enhanced detection performance, it has also led to a notable increase in computational complexity. Furthermore, most of these methods are not complete end-to-end models. In their approaches, traditional hand-crafted filters are employed for image preprocessing. To avoid relying on residual connections and prior knowledge, we propose an end-to-end VGG-style ConvNet. During training, the model utilizes a multi-branch architecture, while it is transformed into a VGG-style ConvNet through structural reparameterization during inference. We conduct extensive experiments on ALASKA KAGGLE dataset and ALASKA II dataset, demonstrating that the proposed method achieves state-of-the-art results in the JPEG domain comparable to other CNN-based steganalyzers such as UCNet and EfficientNet, with clearly better convergence capacity and lower model complexity.

Steganalysis aims to reveal covert communication established via steganography. In the arm race with steganography, steganalysis has evolved from the old-style hand-crafted features set to deep-learning architectures. However, recent studies show 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.

Passive video forensics has become an active topic in recent years. Generally, a pristine video obtained from surveillance cameras or other video recording devices is single compressed. At the same time, double lossy compression will certainly be introduced in tampered videos since it needs to go through recompression to perform tampering on a commonly used compressed video. In the video’s double compression, some traces are left due to the intrinsic effects of recompression. Traditional supervised learning is inefficient because it requires two classes (the pristine and the manipulated) that occur in the video to be exhaustively assigned labels. Actually, compared with pristine videos, manipulated videos with labels are more difficult to obtain. To address this problem, in this paper, one-class classification, which is often used for anomaly detection and only needs the target class, is introduced. We first treat all decompressed video frames as still images and extract subtractive pixel adjacency matrix (SPAM) steganalysis features to detect traces left in the double compression process. Then we adopt a Gaussian density-based one-class classifier since SPAM features extracted from pristine video frames approximately subject to the Gaussian distribution. Furthermore, we improve the robustness of the classifier by using ensemble strategy. Experimental results indicate that our proposed method exceeds other more complex one-class classification methods, and outperforms fully-supervised learning methods only by feeding features from single compressed video frames to our one-class classifier.

Over the past few years, detection performance improvements of deep-learning based steganalyzers have been usually achieved through structure expansion. However, excessive expanded structure results in huge computational cost, storage overheads, and consequently difficulty in training and deployment. In this paper we propose CALPA-NET, a ChAnneL-Pruning-Assisted deep residual network architecture search approach to shrink the network structure of existing vast, over-parameterized deep-learning based steganalyzers. We observe that the broad inverted-pyramid structure of existing deep-learning based steganalyzers might contradict the well-established model diversity oriented philosophy, and therefore is not suitable for steganalysis. Then a hybrid criterion combined with two network pruning schemes is introduced to adaptively shrink every involved convolutional layer in a data-driven manner. The resulting network architecture presents a slender bottleneck-like structure. We have conducted extensive experiments on BOSSBase + BOWS2 dataset, more diverse ALASKA dataset and even a large-scale subset extracted from ImageNet CLS-LOC dataset. The experimental results show that the model structure generated by our proposed CALPA-NET can achieve comparative performance with less than two percent of parameters and about one third FLOPs compared to the original steganalytic model. The new model possesses even better adaptivity, transferability, and scalability.