The field of visual recognition and cryptographic security is witnessing significant advancements with the integration of graph-based reasoning and machine learning techniques. Researchers are exploring new frameworks that enhance structural awareness and feature representation in visual recognition tasks, while also improving the security of cryptographic algorithms against side-channel attacks and leakage abuse. Notably, innovative approaches such as hierarchical graph feature enhancement and frequency analysis are being developed to push the limits of current technologies.

Some noteworthy papers in this area include:

• Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition, which proposes a novel framework for visual recognition tasks.
• Pushing the Limits of Frequency Analysis in Leakage Abuse Attacks, which introduces a generic attack framework for leakage-abuse attacks on schemes that support encrypted range queries.
• Machine Learning-Based AES Key Recovery via Side-Channel Analysis on the ASCAD Dataset, which investigates the application of machine learning techniques for partial key recovery.
• Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning, which proposes a subgraph embedding learning structure for automatic modulation recognition.
• Conditional Cube Attack on Round-Reduced ASCON, which evaluates the secure level of authenticated encryption against cube-like methods.