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.
With the wide spread of deepfake face videos, it has brought huge hidden dangers of trust to national security and social stability. In this paper, the authentication model framework of deepfake face video with spatio-temporal fusion features is proposed. Through three improvements including collecting mixed training samples, training two 2D deep convolutional neural networks with face center clipping images and using 3D deep convolutional neural networks to utilize the inter-frame consistency information, the authentication success rate of deepfake face video is improved. In the experiment two video forgery methods FaceSwap and Deepfakes were selected in the Faceforences ++ dataset to identify the deepfake video of facial feature area and facial edge area, which obtained certain results. Further breakthroughs are expected in the future through the integration of multi-modal data features and the use of large-scale pre-trained models.
E-government information security has attracted tremendous attention recently and most research concentrates on the theory of cryptography. With the development of dynamic e-government system and the widespread use of network, the data in e-government system needs more effective copyright protection and copy protection etc. However, the traditional method of data encryption can not fill the requirements. Digital watermark providing an effective way to solve this problem, offers a new strategy for protecting confidentiality, non-forgeability, authentication, integrity and non-repudiation of government affair information. This paper intensively studies watermark technology for enhancing e-government information security. It proposes a new mechanism based on digital watermark technology and traditional cryptography theory to solve the problem of important information protection in distributed network. A double protection mechanism for important data was developed. The algorithm of watermark embedding, detection, encrypted and decrypted is described. The security of the mechanism is also discussed in detail.
Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction. In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.
The diffusion of AI tools capable of generating realistic DeepFakes (DF) videos raises serious threats to face-based biometric recognition systems. For this reason, several detectors based on Deep Neural Networks (DNNs) have been developed to distinguish between real and DF videos. Despite their good performance, these methods suffer from vulnerability to adversarial attacks. In this paper, we argue that it is possible to increase the resilience of DNN-based DF detectors against black-box adversarial attacks by exploiting the temporal information contained in the video. By using such information, in fact, the transferability of adversarial examples from a source to a target model is significantly decreased, making it difficult to launch an attack without accessing the target network. To back this claim, we trained two convolutional neural networks (CNNs) to detect DF videos, and measured their robustness against black-box, transfer-based, attacks. We also trained two detectors by adding to the CNNs a long short-term memory (LSTM) layer to extract temporal information. Then, we measured the transferability of adversarial examples to-wards the LSTM-networks. The results we got suggest that the methods based on temporal information are less prone to black-box attacks.
Although significant progress has been achieved recently in automatic learning of steganographic cost, the existing methods designed for spatial images cannot be directly applied to JPEG images which are more common media in daily life. The difficulties of migration are mainly caused by the characteristics of the $8\times 8$ DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. Following a domain-transition design paradigm, the policy network is composed of three modules, i.e., pixel-level texture complexity evaluation module, DCT feature extraction module, and mode-wise rearrangement module. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with $8\times 8$ DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature-based and modern CNN-based steganalyzers.
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