With Generative Adversarial Networks (GAN) achieving realistic image generation, fake image detection research has become an imminent need. In this paper, a novel detection algorithm is designed to exploit the structural defect in GAN, taking advantage of the most vulnerable link in GAN generators — the Up-sampling process conducted by the Transposed Convolution operation. The Transposed Convolution in the process will cause the lack of global information in the generated images. Therefore, the Self-Attention mechanism is adopted correspondingly, equipping the algorithm with a much better comprehension of the global information than the other current work adopting pure CNN network, which is reflected in the significant increase in the detection accuracy. With the thorough comparison to the current work and corresponding careful analysis, it is verified that our proposed algorithm outperforms other current works in the field. Also, with experiments conducted on other image-generation categories and images undergone usual real-life post-processing methods, our proposed algorithm shows decent robustness for various categories of images under different reality circumstances, rather than restricted by image types and pure laboratory situation.

Skeleton-based human action recognition has been a popular research field during the past few years. With the help of cameras equipping deep sensors, such as the Kinect, a human action can be represented by a sequence of human skeleton data. Inspired by the skeleton descriptors based on Lie group, a Spatial Temporal Skeleton Transformation Descriptor (ST-STD) is proposed in this paper. The ST-STD describes the relative transformations of skeletons, including the rotation and translation during movement. It gives a comprehensive view of the skeleton in both spatial and temporal domain for each frame. To capture the temporal connections in the skeleton sequence, a Denoising Sparse Long Short Term Memory (DS-LSTM) network is proposed in this paper. The DS-LSTM is designed to deal with two problems in action recognition. Firstly, to decrease the intra-class diversity, the Spatial Temporal Auto-Encoder (STAE) is proposed in this paper to generate representations with higher abstractness. The denoising constraint and the sparsity constraint are applied on both spatial and temporal domain to enhance robustness and to reduce action misalignment. Secondly, to model the action sequence, a three-layer LSTM structure is trained with STAE representations for temporal modelling and classification. The experiments are carried out on four popular datasets. The results show that our approach performs better than several existing skeleton-based action recognition methods, which prove the effectiveness of our method.

Anomaly detection and localization in videos is still a challenging task in computer vision field. Previous methods took this task as a one-class deviation problem, which computed the deviation between test samples and normal patterns. In this paper, an Adaptive Intra-frame Classification Network (AICN) is proposed to transform this task to a multi-class classification problem. The contributions of our method are: 1) AICN is a more effective anomaly evaluation network. It evaluates abnormality based on intra-frame classification results, which reserves more connection information of sub-regions and makes the model outperform previous one-class classifiers. 2) AICN reduces the computational cost. Motion Convolutional Layers (MCLs) and Shape Convolutional Layers (SCLs) are proposed to extract feature maps, which utilize the simplicity of convolution to reduce computational cost. Adaptive Region Pooling Layer (ARPL) is also proposed to reduce the calculation cost of overlapped sub-regions. 3) AICN enhances the adaptiveness of model. Only one multi-class classifier is used rather than multiple one-class classifiers. It makes the network easier to be applied on other scenes and makes the model adaptive to samples with different resolutions. The proposed method is examined on four public datasets with different background complexities and resolutions: UCSD Ped1 dataset, UCSD Ped2 dataset, Avenue dataset and Subway dataset. The results are further compared with previous approaches to confirm the effectiveness and the advantage of our method.

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.

Anomaly detection in videos is still a challenging task among the computer vision community. In this paper, an efficient anomaly detection method based on stacked sparse coding (SSC) with intraframe classification strategy is proposed. Each video is divided into blocks and the Foreground Interest Point (FIP) descriptor is proposed to describe the appearance and motion features for each block. The spatial-temporal features are then encoded with SSC. Specifically, the first stage of SSC encodes the spatial connections among blocks and the second stage of SSC encodes the temporal connections of all frame patches in each block. Finally, an intraframe classification strategy which uses the probabilistic outputs of SVM is proposed to evaluate the abnormality of each block. Contributions of this paper are listed as follows: 1) The FIP descriptor is proposed to describe the features of blocks, which reserves more spatial-temporal information. 2) The SSC encoding method encodes both the spatial and temporal connections of blocks, which makes the features more representative. 3) The intraframe classification strategy keeps the evaluation consistency among blocks and it helps to improve detection performance. The proposed method is examined on four public datasets with different background complexities and resolutions: UCSD Ped1 dataset, UCSD Ped2 dataset, Avenue dataset, and Subway dataset. The results are further compared with previous approaches to confirm the effectiveness and advantages of this method.

The steganography research of videos leads to excellent communication methods for transmitting secret message, and high efficiency video coding(HEVC) video is one popular steganographic carrier. This paper proposes a prediction unit(PU) based wide residual-net steganography(PWRN) for HEVC videos. The visual quality distortion of modifying PUs is theoretically analyzed, which illustrates that modifying PUs only has a little negative effect on visual quality. Therefore, the data hiding method in this paper allows to modify all types of PUs except for 2N2N to each other according to the secret data. In this way, high embedding efficiency is achieved, and the PU distributions in stego-videos can be kept similar to those of cover-videos, which is essential for resisting steganalysis. Meanwhile, a super-resolution convolutional neural network(CNN) with wide residual-net filter(WRNF) is proposed to replace the in-loop filter in HEVC for reconstructing I-pictures, which results in more precisely predicted P-pictures, and it further leads to less bitrate cost and better visual quality of stego-videos. The experimental results show that the proposed PWRN successfully resists the latest PU-targeted steganalysis algorithms, and compared with the state-of-the-art work, PWRN has achieved the lowest bitrate cost and the highest visual quality under the same capacity.