Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

Currently, health management driven by intelligent means is a general demand of social systems. Although a number of researchers have paid attention to such areas, they have primarily focused on improving the performance of intelligent algorithms. Such intelligent algorithms are mostly based on the central computing mode, where all the user data are aggregated together in a central cloud to implement computing tasks. This poses a great threat to personal privacy due to exposure to the outside world. To address this challenge, this work uses a federated learning mechanism and proposes a privacy-preserving social computing framework for health management. User data are deposited in different user terminals to prevent exposure. A group of parameters are pretrained for each terminal in an iteration and are then transferred to the center cloud for updating. After multiple rounds of interactive training between the center cloud and the terminals, a recognition model finishes training for each terminal without direct access to data from other sources. Finally, this work also conducts experiments on a real-world dataset to assess the overall performance of the proposed approach.

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%.

Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

Recently, reversible data hiding in encrypted images (RDHEI) has received widespread attention from researchers. To embed high payload into encrypted images while maintaining sufficient security, a novel RDHEI algorithm in combination with consecutive zero-valued high bit-planes compression, bit-plane swapping as well as block rearrangement is proposed in this paper. The proposed method is the first work to compress global zero-valued high bit-planes in a block-wise manner and adaptively allocate different Huffman indicators based on the occurrence frequency of zero-valued bit-planes so that a higher embedded payload is greatly provided. Unlike existing RDHEI methods embedded with unencrypted auxiliary information, resulting in low security, the bit-plane swapping and block rearrangement are subtly designed to cluster together all embeddable bit-planes, which enables most auxiliary information to be encrypted, largely enhancing the security and facilitating data embedding and data extraction. The experiment results demonstrate that the proposed method outperforms some state-of-the-art RDHEI methods in terms of security and payload. The average payload of the proposed method for two publicly-used datasets including BOSSbase and BOWS-2, are 3.793 bpp and 3.705 bpp, respectively.