arXiv AI By C\^ome-Alexis Puech, S\'ebastien Thuau, Amira Gran, Arthur Mennessier, Siba Haidar, Rachid Chelouah

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

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arXiv AI
Aug 26

STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning

The paper introduces STAIN-FL, a stealthy backdoor attack framework for federated video anomaly detection that uses natural surveillance conditions—such as low light, indoor settings, and crowd density—as contextual triggers. STAIN-FL manipulates anomaly labels and masks gradients to keep clean accuracy low while inducing trigger‑conditioned misclassification. Experiments on UCF‑Crime with I3D features show that sparse attacks remain undetectable, drop clean accuracy by less than 2%, yet achieve over 50% backdoor accuracy for hundreds of rounds under FedAvg and FedProx.

By Ashlinder Kaur, Purnima Murali Mohan, Zengxiang Li, Tram Truong-Huu
arXiv AI
Sep 10

Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection

The paper proposes a lightweight federated multiple‑instance learning (MIL) framework that trains only a compact MIL scorer across distributed clients while using a frozen vision‑language model (VLM) to verify high‑scoring video segments post‑hoc. Two VLM feedback interfaces are explored: a parsed text‑generation interface and a logit‑based interface that derives a continuous anomaly score from next‑token Yes/No probabilities. Experiments on UCF‑Crime with InternVL3.5‑2B and Qwen3‑VL‑2B‑Instruct show that the logit interface consistently improves frame‑level AUC and AP over the MIL baseline without requiring temporal post‑processing, whereas the text‑generation interface is more sensitive to prompts, parsers, and model choice.

By S\'ebastien Thuau, Amira Gran, Siba Haidar, Rachid Chelouah
arXiv Computer Vision
Oct 22

Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models

Vision foundation models such as DINOv2 enable strong few‑shot anomaly detection (FSAD) using a frozen backbone and a non‑parametric k‑nearest‑neighbor (k‑NN) scorer. The paper introduces a white‑box attack framework for k‑NN memory‑bank detectors and proposes an adversarially robust FSAD method that is training‑free with respect to the backbone. The defense combines a PatchShift input augmentation and a FeaturePurifier residual MLP, achieving significant gains in image‑level AUROC (~27%) and pixel‑level PRO (~51%) on MVTec‑AD, VisA, and MPDD while maintaining clean accuracy within 3%. whyItMatters:"The work addresses the previously unstudied few‑shot regime for robust anomaly detection, providing a practical, training‑free defense that matches full‑shot adversarially‑trained baselines and improves performance under adversarial attacks."

By Akib Mohammed Khan, Bartosz Krawczyk
arXiv Machine Learning
Oct 23

MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models

MoG-VLM is a motion‑guided vision‑language model designed for efficient video anomaly detection. It uses a lightweight motion signal, Temporal Gradient Magnitude, to perform content‑aware sampling and activates dense multi‑frame VLM reasoning only around frames with high anomaly likelihood via a hierarchical dual‑stream design. The method achieves state‑of‑the‑art AUC‑ROC scores on four benchmarks and delivers a 1.31× inference speedup on XD‑Violence.

By Khaled Waleed Dawoud, Muhammad Zaigham Zaheer, Khan Muhammad, Muhammad Haris Khan