Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since adding perturbations to stegos may lead to messages extraction failure due to properties of syndrome-trellis codes (STC), the existing adversarial examples are derived from covers or their enhanced versions, while those stegos are not fully utilized. In this paper, we propose a new adversarial embedding scheme for image steganography. Unlike those related works, we first combine multiple gradients of cover and generated stegos to determine the directions of cost modifications. Next, instead of adjusting all or a random part of embedding costs in existing works, we carefully select the candidate costs according to the amplitudes of cover gradients and their costs. Extensive experimental results demonstrate that by adjusting a tiny part of embedding costs (less than 5% in most cases), the proposed method can significantly improve the security of five modern steganographic methods evaluated on both re-trained CNN-based and traditional steganalyzers, and achieve much better security performances compared with related methods. In addition, the security performances evaluated on different image database show that the generalization of the proposed method is good.

Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of $4\times 4$ pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.

Performing detection on surveillance videos contributes significantly to the goals of safety and security. However, performing detection on unprotected surveillance video may reveal the privacy of innocent people in the video. Therefore, striking a proper balance between maintaining personal privacy while enhancing the feasibility of detection is an important issue. One promising solution to this problem is to encrypt the surveillance videos and perform detection on the encrypted videos. Most existing encrypted signal processing methods focus on still images or small data volumes; however, because videos are typically much larger, investigating how to process encrypted videos is a significant challenge. In this article, we propose an efficient motion detection and tracking scheme for encrypted H.264/AVC video bitstreams, which does not require the previous decryption on the encrypted video. The main idea is to first estimate motion information from the bitstream structure and codeword length and, then, propose a region update (RU) algorithm to deal with the loss and error drifting of motion caused by the video encryption. The RU algorithm is designed based on the prior knowledge that the object motion in the video is continuous in space and time. Compared to the existing scheme, which is based on video encryption that occurs at the pixel level, the proposed scheme has the advantages of requiring only a small storage of the encrypted video and has a low computational cost for both encryption and detection. Experimental results show that our scheme performs better regarding detection accuracy and execution speed. Moreover, the proposed scheme can work with more than one format-compliant video encryption method, provided that the positions of the macroblocks can be extracted from the encrypted video bitstream. Due to the coupling of video stream encryption and detection algorithms, our scheme can be directly connected to the video stream output (e.g., surveillance cameras) without requiring any camera modifications.

In cloud computing, videos may be in an encrypted format to protect privacy. Therefore, encrypted video processing is an important application in secure cloud computing. In this paper, we focus on parameter estimation and anomaly detection in an encrypted video bitstream. By analyzing the common properties of video encoding frameworks and the format-compliant encryption schemes, we propose an anomaly detection scheme for encrypted video bitstream with format-compliant encryption. From the encrypted bitstream, we extract three types of complementary features, i.e., the macroblock sizes, the macroblock partitions, and the motion vector difference magnitude, and then propose a method to combine these three features. The proposed detection and localization scheme does not involve video decryption, full decompression, or an interactive protocol, which makes it efficient. Our scheme is also compatible with different video encryption methods. To accelerate the running time, we develop a parallel implementation for our scheme. The experimental results show that our method achieves good running time and detection rate performance.