4 papers
TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge
Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky +5
Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the atta…
PROVE-RT: Generating Mechanized Theorem Prover Scripts for Real-Time Systems using LLMs
Sadat Shahriyar, Shareef Ahmed, Abdullah Al Arafat
Schedulability analysis is essential for certifying real-time systems, but existing tests are often developed through pen-and-paper proofs that are difficult to scale, validate, an…
CertMask: Certifiable Defense Against Adversarial Patches via Theoretically Optimal Mask Coverage
Xuntao Lyu, Ching-Chi Lin, Abdullah Al Arafat +3
Adversarial patch attacks inject localized perturbations into images to mislead deep vision models. These attacks can be physically deployed, posing serious risks to real-world app…
Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains
Sabbir Ahmed, Mamshad Nayeem Rizve, Abdullah Al Arafat +4
Semi-Supervised Federated Learning (SSFL) is gaining popularity over conventional Federated Learning in many real-world applications. Due to the practical limitation of limited lab…