ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel restoration-assisted framework for image tampering localization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart of the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The proposed framework is evaluated by fighting against JPEG compression, the most commonly used post-processing. Extensive experimental results show that ReLoc can significantly improve the robustness against JPEG compression. The restoration module in a well-trained ReLoc model is transferable. Namely, it is still effective when being directly deployed with another tampering localization module.Comment: 12 pages, 5 figure

Performance Enhancement of Multipath TCP for Wireless Communications with Multiple Radio Interfaces

ArticleMultipath TCP (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the emerging mobile devices with various radio interfaces and to improve resource utilization as well as connection robustness. The existing multipath congestion control algorithms, however, are mainly loss-based and prefer the paths with lower drop rates, leading to severe performance degradation in wireless communication systems where random packet losses occur frequently. To address this challenge, this paper proposes a new mVeno algorithm, which makes full use of the congestion information of all the subflows belonging to a TCP connection in order to adaptively adjust the transmission rate of each subflow. Specifically, mVeno modifies the additive increase phase of Veno so as to effectively couple all subflows by dynamically varying the congestion window increment based on the receiving ACKs. The weighted parameter of each subflow for tuning the congestio

Formulation of energy loss due to magnetostriction to design ultraefficient soft magnets

Tsukahara H., Huang H., Suzuki K., et al. Formulation of energy loss due to magnetostriction to design ultraefficient soft magnets. NPG Asia Materials 16, 19 (2024); https://doi.org/10.1038/s41427-024-00538-8.The mechanism of energy loss due to magnetostriction in soft magnetic materials was analytically formulated, and our experiments validated this formulation. The viscosity of magnetic materials causes the resistive force acting on magnetic domain walls through strain due to magnetostriction, and magnetic energy is eventually dissipated by friction even without eddy currents. This energy loss mechanism explains the frequency dependence of the excess loss observed in the experiments, and the excess loss is dominated by the contribution of magnetostriction when the magnetostriction constant exceeds approximately 20 ppm. The random anisotropy model was extended by considering the effect of local magnetostriction as a correction to the magnetocrystalline anisotropy. The effect of magnetostriction was considerably suppressed by the exchange-averaging effect. The estimated effective random magnetoelastic anisotropy for nanocrystalline α-Fe reached as low as 18.6 J/m3, but this static effect could not explain the high excess loss at high frequencies observed in the experiments. The results of this research could provide new design criteria for high-performance soft magnetic materials based on low magnetostriction to reduce the excess loss