TIFS Volume 20 | 2025

Recently, stacked intelligent metasurfaces (SIMs) have aroused widespread discussions as an innovative technology for directly processing electromagnetic (EM) wave signals. By stacking multiple programmable metasurface layers, an SIM has the ability to provide additional spatial degrees of freedom without the introduction of expensive radio-frequency chains, which may outperform reconfigurable intelligent surfaces (RISs) with single-layer structures. 

Modern integrated circuits (ICs) require a complex, outsourced supply-chain, involving computer-aided design (CAD) tools, expert knowledge, and advanced foundries. This complexity has led to various security threats, such as Trojans inserted by adversaries during outsourcing, but also run-time threats like physical probing. Our proposed design-time solution, DEFense , is an extensible CAD framework for holistic assessment and proactive mitigation of multiple prominent threats.

Aggregating metadata in the ciphertext field is an attractive property brought by homomorphic encryption (HE) for privacy-sensitive computing tasks, therefore, research on the next-generation wireless networks has treated it as one of the promising cryptographic techniques for various scenarios. However, existing schemes are far from being deployed in various computing scenarios due to their high computational complexity and ciphertext expansion, especially for bandwidth-limited and latency-sensitive wireless scenarios.

The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity. 

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters.