A method using randomized image subsets enables PRNU-based source camera attribution on Patch-Match anonymized image sets.
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REALM is the first unified red-teaming benchmark for physical-world VLMs that aligns diverse attack methods via an agentic target-generation pipeline and evaluates them on shared datasets showing text/typographic attacks as most effective.
DIPBox is the first multi-scale testing framework for detecting adversarial dataset regeneration via four similarity metrics, backed by learning-theoretic analysis of utility-divergence trade-offs.
MimeLens uses position-agnostic BERT encoders pretrained on random-offset binary windows to output one of 125 libmagic MIME labels, beating Magika on full files and enabling accurate classification on mid-file fragments.
DIST-FL distributes TEE-guarded servers into an append-only ledger to ensure linearizable FL aggregation and counter rollback plus I/O attacks while matching single-TEE speed.
GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.
Empirical study finds OV-OD robustness driven by vision backbone and image domain via layer-wise feature collapse analysis, validated with a low-parameter robustness improvement on real data.
PenTiDef integrates distributed differential privacy, autoencoder-based latent semantic representations with CKA and K-Means clustering for malicious update detection, and blockchain-orchestrated secure FedAvg to deliver higher detection accuracy and F1-score than FLARE and FedCC under up to 40%
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.
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GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
GRASP detects anomalies in system provenance graphs via self-supervised executable prediction from two-hop neighborhoods, outperforming prior PIDS on DARPA datasets by identifying all documented attacks where behaviors are learnable plus additional unlabeled suspicious activity.