Research
Academic papers, new techniques, benchmarks, and theoretical findings in AI/LLM security.
Academic papers, new techniques, benchmarks, and theoretical findings in AI/LLM security.
2,033 items
The Journal of Information Security and Applications (Volume 100, July 2026) published a paper by Jonathan Sharp, Baker Womack, Csilla Farkas, Dipankar Dasgupta and Arunava Roy on a context-aware and adaptive multi-factor authentication model. The source text provided does not describe the method, findings or numbers beyond this bibliographic information.
This research studies discrete distribution estimation under utility-optimized local differential privacy (ULDP), which enforces LDP on sensitive data while allowing more accurate inference on non-sensitive data. The authors completely characterize the privacy-utility trade-off, proving the converse with a generalized uniform asymptotic Cramér–Rao lower bound and a reduction to extremal ULDP mechanisms. They achieve the bound with utility-optimized block design (uBD) schemes, modifications of the block design mechanism, paired with a score-based linear estimator.
This paper proposes the Modality-aware Graph Reasoning Network (MGRNet) for multi-modal object Re-Identification. The method builds modality-aware graphs over image patches, swaps selective graph nodes to reduce the effect of low-quality local features, and propagates multi-modal information to reconstruct missing modalities. The authors report state-of-the-art results on four benchmarks: RGBNT201, Market1501-MM, RGBNT100 and MSVR310.
This paper proposes a secure, asynchronous structured skyline predicate for computing skyline queries over encrypted vertically federated data, protecting both the dataset and the skyline from unauthorized access. The approach relies on vertical dominance and truth-value conversion, and adds an optimization that balances security against efficiency. Evaluation across parameters reports gains in traversal overhead and in expensive ciphertext operations.
Researchers present key recovery attacks on DIZY-80 and DIZY-128, a small-state stream cipher from 2023. The attacks exploit reaching a weak state mid-initialization via chosen IVs and use Hellman tables. They show DIZY-80 and DIZY-128 offer only 65 and 86-bit security, versus the 80 and 112-bit claimed by the designers. The paper also proposes DIZYa, an improved variant.
The source is a bibliographic listing for a July 2026 paper in the Journal of Information Security and Applications, Volume 100, by Daniel Commey, Sena G. Hounsinou and Garth V. Crosby, titled "PUFZIN: Secure and scalable blockchain-IoT with PUFs and zero-knowledge proofs." The provided text contains no abstract, method or findings.
Researchers Abeer Qashou, Nurhidayah Bahar, and Hazura Mohamed published a paper in Computers & Security, available online 21 May 2026, on data security risks for mobile cloud computing in higher education. The source text provided contains only the title, publication details, and author list, so the study's method and findings are not described.
This article studies how a phased array radar should schedule its time across joint search and tracking tasks while under adversarial jamming. It formulates a multi-step dynamic optimization model with a receding-horizon strategy, solved by combining Pontryagin's maximum principle with ADMM. Numerical simulations reportedly show higher tracking accuracy, jamming resilience and total utility than several comparison methods.
Researchers introduce Federated Contrastive Diffusion Prototypes (Fed-CDP), a method that has the server turn aggregated client prototypes into synthesized features for robust federated learning. On CIFAR-100 under severe heterogeneity (α=0.1), Fed-CDP surpasses leading methods by nearly 5% in standard accuracy and over 9% in robust accuracy under Projected Gradient Descent attacks.
Researchers propose a cage-based adversarial deformation framework that generates semantically consistent perturbations to 3D point cloud classifiers, aiming to resolve the trade-off between imperceptibility and attack strength found in point-wise distance methods. Experiments on ModelNet40, ShapeNet-Part and ScanObjectNN report consistently high attack success rates, improved point uniformity and fewer local geometric distortions, with the perturbations remaining effective against various defense methods.
Researchers propose EA-APO, a proactive defense that optimizes adversarial perturbations on a white-box surrogate model and applies them to source face images. The perturbations are meant to disrupt both face-swapping and face attribute editing, including against unseen black-box target models. The authors report generalization across multiple face-swapping and attribute-editing models, including commercial ones, and robustness to common post-processing and real-world social media transmission.
Fix: The source proposes an improved variant, DIZYa, which it says provides better resistance to all known attacks, including those on DIZY, while keeping DIZY's characteristics. The source does not describe a patch or configuration change for existing DIZY deployments.
The source is only publication metadata for a paper titled DWT-AMSA on robust image steganography, listing a July 2026 date, the Journal of Information Security and Applications (Volume 100), and five authors. It gives no question, method or findings.
Mahender Kumar, Ruby Rani, Gregory Epiphaniou and Carsten Maple published Adaptive Trust-Aware SOC Human–AI Teaming for resilient operations in Computers & Security, available online on 21 May 2026. The source text provided contains only publication metadata and no description of the method or findings.
The paper proposes FairRoP, a client selection scheme for federated learning that aims to improve fairness and robustness at the same time. It models the problem as a multi-objective optimization solved with an ε-greedy Thompson Sampling Multi-Armed Bandit, using fairness awareness, attack detection and q-Balance submodules. Experiments on real datasets reportedly show significant gains in fairness and robustness over state-of-the-art methods, and the approach integrates with other aggregation algorithms.