arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).
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.
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.
arXiv:2606. 14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both.
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."
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.
The paper introduces XAVAD, a unified approach for explainable anomaly detection in anonymized crowd video monitoring. It combines zero‑shot anonymization, anomaly type categorization, spatial localization, and scene context description using multimodal CLIP embeddings, LLM‑assisted prompting, and a prompt‑guided patching autofocus mechanism. Experiments on public CCTV datasets show XAVAD outperforms state‑of‑the‑art methods and offers insights into privacy‑explainability trade‑offs.
The paper investigates federated adversarial training (AT) for vision transformers, a topic not previously explored in federated learning (FL). It evaluates various transformer architectures and aggregation strategies, and introduces FedWAvg, an extension of FedAvg that weights client updates based on similarity of their last-layer representations. Experiments demonstrate that FedWAvg yields higher robust accuracy than existing aggregation methods in non‑IID settings.
MS2CAST is a semi‑supervised video anomaly detection framework that fuses multi‑scale cross‑attention between a vision transformer (MViTv2‑S) and a CNN (ConvNeXt‑T) for spatial encoding, and replaces optical flow with two interchangeable spatiotemporal modules—SwinLSTM and VMRNN—operating in latent space. The training objective combines L1 loss, Gradient Difference Loss, and Multi‑Scale SSIM to enforce pixel‑level accuracy, sharpness, and structural fidelity. Experiments on UCSD Ped2, CUHK Avenue, and ShanghaiTech show competitive ROC‑AUC scores while using less than 1 GB of GPU memory.
Efficient Zero‑Shot Video Anomaly Detection via Signal‑Aware Adaptive Fusion (SAAF) introduces a framework that leverages dual‑signal adaptive sampling—delta and drift signals—to detect sudden and gradual anomalies without training data. It employs a threshold‑gated cascaded architecture that first applies lightweight frame‑level scoring before invoking costly video‑level analysis only when needed, and a signal‑aware weighted fusion mechanism that adapts score combination based on the triggering signal. Across four benchmarks, SAAF achieves state‑of‑the‑art accuracy on XD‑Violence, MSAD, and ShanghaiTech while delivering significant throughput improvements over prior zero‑shot methods.
FedVideoMAE is a federated learning framework for short‑form video moderation that keeps raw videos on the device and uses a frozen VideoMAE backbone with lightweight LoRA and prompt parameters. Each training round performs self‑supervised masked video reconstruction while applying client‑side differential privacy and pairwise masked aggregation of adapter updates, keeping violence labels out of the federation. On the RWF‑2000 dataset, the method reduces the model‑state payload by 28.3× and achieves 77.25% accuracy without privacy mechanisms, dropping to 65.25–66.00% under differential privacy and secure aggregation.
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance.