CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis.Comment: Submitted to IEEE Transactions on Information Forensics and Securit

Investigation of Key Parameters for Hydraulic Optimization of an Inlet Duct Based on a Whole Waterjet Propulsion Pump System

The hydraulic performance of an inlet duct directly affects the overall performance of a waterjet propulsion system. Key parameters for the hydraulic optimization of the inlet duct are explored using the computational fluid dynamics (CFD) technology to improve the hydraulic performance of the waterjet propulsion system. In the CFD simulation and experiment, an inlet duct with different flow and geometric parameters is simulated. By comparing grid sensitivity and different turbulence models, a suitable grid size and a turbulence model are determined. The comparison between the numerical simulation and the experiment shows that the numerical results are reliable. The results of the calculation and analysis of different speed cases show that the ship speed affects the efficiency of the waterjet propulsion system. In particular, the system efficiency increases first and then decreases with an increase in the ship speed. Under the conditions of constant ship speed and rotational speed, the influence of the length and dip angle of the inlet duct on the waterjet propulsion system is investigated using a single factor method. The results show that the dip angle has an obvious effect on the hydraulic performance of the inlet duct, and an extremely small angle of inclination will lead to poor flow patterns in the inlet passage. When the length is approximately six times the inlet duct outlet diameter, and the dip angle is 30°–35°, the hydraulic performance of the waterjet propulsion pump system is satisfactory

Hydraulic Characteristics and Measurement of Rotating Stall Suppression in a Waterjet Propulsion System

Rotating stall as a kind of ship stall causes noise, vibration and unstable operation of a waterjet propulsion system and sometimes it can even cause fracture of blades and destruction of other flow passage components. To investigate the suppression of the rotating stall, a complete 3-D waterjet propulsion system model has been developed which contains an inlet passage, a propulsion pump and a nozzle. Hydraulic performance and flow characteristics are predicted by using a numerical simulation, which is in good agreement with the experimental results. For suppressing the rotating stall, separators are set in the outlet of the inlet passage. The analysis has shown the following: the rotating stall zone is found to be significant on the external characteristic curve in the low flow rate condition. Also, in the same condition a large scale flow separation region occurs in the propulsion pump, which is more intense at the rim of the impeller. The rotating stall of the propulsion pump system is controlled by setting separators at the outlet of the inlet passage. The recommended parameters of the separators are 0.5 D0 (length), 0.1 D0 (height), 0.4 D0 (location), 0.025 D0 (thickness), 4 (number of separators), where D0 presents the outlet diameter of the inlet passage

HVS Revisited: A Comprehensive Video Quality Assessment Framework

Video quality is a primary concern for video service providers. In recent years, the techniques of video quality assessment (VQA) based on deep convolutional neural networks (CNNs) have been developed rapidly. Although existing works attempt to introduce the knowledge of the human visual system (HVS) into VQA, there still exhibit limitations that prevent the full exploitation of HVS, including an incomplete model by few characteristics and insufficient connections among these characteristics. To overcome these limitations, this paper revisits HVS with five representative characteristics, and further reorganizes their connections. Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed. It works in a domain-fusion design paradigm with advanced network structures. On the side of the spatial domain, the visual saliency module applies SAMNet to obtain a saliency map. And then, the content-dependency and the edge masking modules respectively utilize ConvNeXt to extract the spatial features, which have been attentively weighted by the saliency map for the purpose of highlighting those regions that human beings may be interested in. On the other side of the temporal domain, to supplement the static spatial features, the motion perception module utilizes SlowFast to obtain the dynamic temporal features. Besides, the temporal hysteresis module applies TempHyst to simulate the memory mechanism of human beings, and comprehensively evaluates the quality score according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module towards the proposed framework.Comment: 13 pages, 5 figures, Journal pape