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This research addresses the problem of incomplete knowledge graphs (databases of connected facts about entities) by proposing a new model called TEDD that predicts missing relationships between entities. The model combines both structural information from the graph and text information, and uses a specialized transformer technique (BERT, a language processing model) to reduce computational costs and handle entities that change over time in dynamic knowledge graphs.

This research paper argues that the real problem with machine learning classifiers isn't that robustness (resistance to adversarial attacks, where small malicious changes trick the AI) and accuracy are fundamentally opposed, but rather that continuous functions (smooth mathematical functions without jumps or breaks) cannot achieve both properties simultaneously. The authors propose that effective robust and accurate classifiers should use discontinuous functions (functions with breaks or sudden changes) instead, and show that understanding this continuity property is crucial for building, analyzing, and testing modern machine learning models.

This research presents ABAE-RTN, a deep learning framework that improves security in wireless radio networks by using adaptive beamforming (technology that focuses radio signals toward intended receivers) and autoencoders (neural networks that learn to compress and reconstruct data) to protect against eavesdropping. The system adds artificial noise to disrupt attackers while maintaining communication quality, and adjusts its signal patterns in real time to handle changing channel conditions. Testing shows it outperforms other AI approaches like LSTM (long short-term memory, a type of neural network good at processing sequences) in protecting wireless communications.

ATLAS Data v5.1.0 is an updated framework that documents security threats and defenses related to AI systems, now containing 16 tactics, 84 techniques, and 32 mitigations. The update adds new attack methods targeting AI, such as prompt injection (tricking an AI by hiding instructions in its input), deepfake generation, and data theft from AI services, along with new defensive measures like human oversight of AI agent actions and restricted permissions for AI tools. It also includes 42 real-world case studies showing how these attacks and defenses apply in practice.

Researchers discovered a new attack called FUBA (federated unlearning backdoor attack) that exploits a privacy feature in federated learning (a technique where multiple parties train an AI model together without sharing their raw data). The attack uses malicious unlearning requests, which are supposed to let participants remove their data from a trained model, to secretly inject backdoors (hidden harmful behaviors) into the model instead. The attack is difficult to detect because it hides from existing security defenses.

This research proposes FedFFTNet, a system for identifying which camera model took a photo by using federated learning (a technique where AI models train on data kept private across multiple devices rather than sharing raw data centrally). The system uses a lightweight deep learning architecture and a Laplacian-based patch selection strategy (focusing on sharp, detailed areas of images) to identify cameras while maintaining privacy, achieving very high accuracy rates on standard benchmark datasets.