In this work, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking both spatial and temporal information into consideration, and a 3D Convolutional Neural Network (3D CNN) architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods.

Detection of double compression with the same coding parameters is a very challenging problem in video forensics, since traces of recompression operations are extremely slight in this case. To solve this problem, we first analyze degradation mechanisms during recompression. It is observed that the video quality tends to become nearly unchanged after multiple recompressions with the same coding parameters. The degree of quality degradation is used to distinguish single and double compressed videos. This property can be described using the convergent tendency of video data to unchanged states after continuous recompressions. For MPEG videos, statistical features of rounding and truncation errors are extracted from the intra-coding process while macroblock-mode based features are obtained from the inter-coding process. The final feature is generated by concatenating these two sets of features to provide robust detection capability. Then, extracted features are fed to the SVM classifier to obtain the final detection result. In addition, aforementioned features are modified and extended to detect double compression on H.264 videos based on the unique coding techniques developed in the H.264 standard, such as intra-prediction. Several public available YUV sequences are used to construct double compression databases with three popular coding standards, including MPEG-2, MPEG-4, and H.264. In experiments, the proposed method outperforms several state-of-the-art methods for different compression qualities and rate control schemes. Experimental results demonstrate the proposed method has more robust detection capability of double compression under various encoding configurations.

In this letter, a deep-learning-based pipeline is proposed to distinguish photographics (PGs) from computer-graphics (CGs) combining convolutional neural network (CNN) and recurrent neural network (RNN). In the preprocessing stage, the color space transformation and the Schmid filter bank are utilized to extract chrominance and luminance components, which suppress the irrelevant information of various image contents for the CG identification task. Then, a dual-path CNN architecture is designed to learn joint feature representations of local patches for exploiting their color and texture characteristics. To extract the global artifact, the directed acyclic graph RNN is applied to model the spatial dependence of local patterns. Finally, the output score of RNN is used to identify the input sample. The CG/PG dataset is constructed by collecting samples from the Internet. Experimental results show that the proposed framework can outperform state-of-the-art methods on identification ability of CGs, especially for images with low resolution.

This study focuses on the quasisynchronization problem for reaction–diffusion neural networks (RDNNs) in the presence of deception attacks. Under deception attacks, a time–space sampled-data (TSSD) control mechanism is proposed for RDNNs. Compared with traditional control strategies, the proposed control mechanism can not only save network bandwidth but also improve the cybersecurity of communications. Inspired by Halanay’s inequality, a new inequality is proposed, which can be effectively applied to the quasisynchronization problem for dynamical systems. Then, by using this inequality and the Lyapunov functional approach, quasisynchronization criteria are set for RDNNs. The desired control gain is gained from solving a group of linear matrix inequalities. Moreover, in the absence of deception attacks, the exponential synchronization problem is studied for RDNNs. In the end, simulation results are given to demonstrate the usefulness of the theoretical analysis.

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this article, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-Based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.

Detection of double compression is regarded as one primary step in analyzing the integrity of digital videos, which is of prominent importance in video forensics. However, current methods are vulnerable with the severe lossy quantization in the recompression process such that it is challenging to obtain reliable frame-wise detection results, especially for the high efficiency video coding (HEVC) standard. In view of these issues, in this paper, a hybrid neural network is proposed to reveal abnormal frames in HEVC videos with double compression by learning robust spatio-temporal representations from coding information in the compression domain. Based on the statistical analysis of Coding Units (CUs), it is interesting to find that HEVC video streams contain "rich" coding information that could be leveraged to identify abnormal traces caused by double compression. Two types of coding information maps, including CU Size Map (CSM) and CU Prediction mode Map (CPM), are exploited. In contrast with the conventional paradigm relying on pixel-level representations of decoded frames, CSMs and CPMs of a short-time video clip are treated as the input, aiming to achieve high robustness against recompression of low quality. In our hybrid neural network, an attention-based two-stream residual network is proposed to learn hierarchical representations from CSM and CPM, which are then jointly optimized by the attention-based fusion module. Finally, the temporal variation is modeled by Long Short-Term Memory (LSTM) to obtain frame-wise detection results. We have conducted extensive experiments considering various video content and coding parameters, such as bitrates and sizes of Group of Picture. Experimental results show that our approach can obtain state-of-the-art performance compared with conventional methods, especially when videos are recompressed in the low bitrate coding scenarios.