ExS-GAN: Synthesizing Anti-Forensics Images via Extra Supervised GAN.

So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.

Stability analysis of hybrid time-delay systems using homogeneity property.

In this paper, stability analysis is studied for hybrid time-delay systems (HTDSs) with homogeneity. Using Lyapunov-Krasovskii functional method, a novel and relaxed condition is derived to analyze pre-asymptotic stability of the considered systems with homogeneity. Then, the connections of solutions are established between the systems with different sizes. Based on the established connections, some homogeneous properties are introduced to analyze stability of the HTDSs with different sizes or degrees, and then some necessary and sufficient stability conditions are obtained. Finally, numerical examples are provided to show the effectiveness of the results obtained in this paper.

Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L1 norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L1 norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.

Asynchronous Distributed Finite-Time H∞ Filtering in Sensor Networks With Hidden Markovian Switching and Two-Channel Stochastic Attack.

This article investigates the asynchronous distributed finite-time H∞ filtering problem for nonlinear Markov jump systems over sensor networks under stochastic attacks. The stochastic attacks, called two-channel deception attacks, exist not only between the Markov jump plant and the sensors but also among the sensors. It is assumed that the mode of the filter relies on, but is asynchronous with, that of the Markov jump plant. First, we establish a filtering error system that combines the Markov jump plant with the asynchronous filtering system. Then, we present an asynchronous distributed filter, which ensures the filtering error system mean-square finite-time bounded and satisfies a prescribed H∞ performance level under the two-channel attacks. Finally, an example is given to illustrate the effectiveness of the presented filter.

ASIF-Net: Attention Steered Interweave Fusion Network for RGB-D Salient Object Detection.

Salient object detection from RGB-D images is an important yet challenging vision task, which aims at detecting the most distinctive objects in a scene by combining color information and depth constraints. Unlike prior fusion manners, we propose an attention steered interweave fusion network (ASIF-Net) to detect salient objects, which progressively integrates cross-modal and cross-level complementarity from the RGB image and corresponding depth map via steering of an attention mechanism. Specifically, the complementary features from RGB-D images are jointly extracted and hierarchically fused in a dense and interweaved manner. Such a manner breaks down the barriers of inconsistency existing in the cross-modal data and also sufficiently captures the complementarity. Meanwhile, an attention mechanism is introduced to locate the potential salient regions in an attention-weighted fashion, which advances in highlighting the salient objects and suppressing the cluttered background regions. Instead of focusing only on pixelwise saliency, we also ensure that the detected salient objects have the objectness characteristics (e.g., complete structure and sharp boundary) by incorporating the adversarial learning that provides a global semantic constraint for RGB-D salient object detection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably against 17 state-of-the-art saliency detectors on four publicly available RGB-D salient object detection datasets. The code and results of our method are available at https://github.com/Li-Chongyi/ASIF-Net.

Transportation Spherical Watermarking.

During the past twenty years, there has been a great interest in the study of spread spectrum (SS) watermarking. However, it is still a challenging task to design a secure and robust SS watermarking method. In this paper, we first define a family of secure SS watermarking methods, named as spherical watermarking (SW). The watermarked correlation of SW is defined to be uniformly distributed on a spherical surface, and this makes SW be key-secure against the watermarked-only attack. Then, we propose an implementation of SW, called transportation SW (TSW), which is designed to decrease embedding distortion in a recursive manner using the transportation theory, meanwhile keeping the security of SW. Moreover, we present a theoretical analysis of the embedding distortion and robustness of the proposed method. Finally, extensive experiments are conducted on simulated signals and real images. The experimental results show that TSW is more robust than existing secure SS watermarking methods.

A Study on the Security Levels of Spread-Spectrum Embedding Schemes in the WOA Framework.

Security analysis is a very important issue for digital watermarking. Several years ago, according to Kerckhoffs' principle, the famous four security levels, namely insecurity, key security, subspace security, and stego-security, were defined for spread-spectrum (SS) embedding schemes in the framework of watermarked-only attack. However, up to now there has been little application of the definition of these security levels to the theoretical analysis of the security of SS embedding schemes, due to the difficulty of the theoretical analysis. In this paper, based on the security definition, we present a theoretical analysis to evaluate the security levels of five typical SS embedding schemes, which are the classical SS, the improved SS (ISS), the circular extension of ISS, the nonrobust and robust natural watermarking, respectively. The theoretical analysis of these typical SS schemes are successfully performed by taking advantage of the convolution of probability distributions to derive the probabilistic models of watermarked signals. Moreover, simulations are conducted to illustrate and validate our theoretical analysis. We believe that the theoretical and practical analysis presented in this paper can bridge the gap between the definition of the four security levels and its application to the theoretical analysis of SS embedding schemes.

Analyzing the Effect of JPEG Compression on Local Variance of Image Intensity.

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.