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Multimedia images have become an important way for the sharing of digital information. However, advanced editing tools provide easy methods for malicious content modification, resulting in less viable information. Therefore, there is an urgent need to design an algorithm for image tampering detection and localization, which is capable of locating the authenticity region of the received image, thus delivering the accurate information and assisting in correct decision making in industries and other fields. In this article, a novel dual-pathway multiscale network (DPMSN) is proposed for the image forgery detection, which mainly focuses on extracting the edge information. In particular, a dual-pathway structure is deployed to align visual features in red, green and blue (RGB) space and edge information in LAB space, where a coarse prediction mask is generated to promote accurate localization of the forged regions. By applying the variation convolution operators, comprehensive attention can be paid to various forged regions in multiple sizes. Moreover, in the multiscale fusion module, features at different stages and other low-level information are sufficiently fused to realize a robust presentation of the forged regions. Experimental results show the effectiveness of DPMSN as compared with other state-of-the-art image forgery detection models and the great robustness when facing image attacks, which means DPMSN is a trustworthy forgery detection approach in the industrial field.

This article is concerned with the privacy-preserving distributed economic dispatch problem of microgrids. The main goal of this work is to develop a privacy-preserving distributed optimization algorithm over directed networks, aiming to achieve supply-demand balance at the lowest economic cost under practical constraints while preventing the leakage of power-sensitive information. For this purpose, a distributed optimization algorithm with a constant step size is proposed by combining the decentralized exact first-order algorithm with the push-sum protocol, which offers an advantage in terms of fast convergence. In addition, to ensure privacy preservation, a state-decomposition approach is employed by randomly dividing the state into two parts, where only partial state information is transmitted. Moreover, the effectiveness of the privacy-preserving scheme against honest-but-curious nodes and external eavesdroppers is demonstrated through rigorous analysis. Finally, simulation studies demonstrate the validity and superiority of the developed privacy-preserving distributed algorithm.

A transformer-less soft-switching high-gain pulsewidth-modulation (PWM) converter is proposed in this article. The proposed converter is derived from a single-switch dual-inductor transformer-less boost converter by replacing the complementary diode connected to the switch with a synchronous rectifier switch. Moreover, the peak value of the reverse current of the rear-end inductor in continuous bidirectional conduction mode (CBCM) is regulated to exceed the input inductor valley current. Based on these two measures, the zero-voltage switching (ZVS) for all switches and the natural turn-off for the output diode are realized, and the effective duty cycle is enhanced. Besides, the proposed ZVS converter has the merits of fewer power components count, reduced voltage stress, and lower cost. Furthermore, the ringing issue in the original topology under light load or high input voltage conditions is eliminated. The operation principle, steady-state characteristics, dynamic model, soft-switching operation, and realization conditions are analyzed in detail, and subsequently, the parameter design method is elaborated. The proposed topology demonstrates an enhanced efficiency performance over wide operating operations with a maximum efficiency of 97.1%.