arXiv Machine Learning September 15, 2026 By Yuchao Hou (Shanxi Normal University, Taiyuan, China), Zixuan Zhang (Shanxi Normal University, Taiyuan, China), Jie Wang (Shanxi Normal University, Taiyuan, China), Wenke Huang (Nanyang Technological University, Singapore, Singapore), Lianhui Liang (Guangxi University, Nanning, China), Di Wu (La Trobe University, Melbourne, Australia), Zhiquan Liu (Jinan University, Guangzhou, China), Youliang Tian (Guizhou University, Guiyang, China), Jianming Zhu (Central University of Finance and Economics, Beijing, China), Jisheng Dang (Lanzhou University, Lanzhou, China), Junhao Dong (Nanyang Technological University, Singapore, Singapore), Zhongliang Guo (University of St Andrews, St Andrews, United Kingdom)

Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

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safety

arXiv AI

Aug 10

TOFD: Target-Oriented Feature Decoupling against Poisoning Attacks in Split Federated Learning

arXiv:2608. 07274v1 Announce Type: cross Abstract: Split Federated Learning (SFL) facilitates privacy-preserving collaborative training with reduced client-side overhead.

By Yuhan Xie, Jingrong Huang, Chen Lyu

benchmarks safety

arXiv Machine Learning

Sep 7

Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

The paper introduces FedIoC, a federated learning framework that embeds structured threat indicators into gradient updates using a supervised contrastive loss. By aligning gradients from clients that share indicators for the same attack campaign, the server can cluster updates via cosine similarity to recover global campaign patterns without transmitting sensitive indicators. Experiments on two public threat‑detection benchmarks show that the server successfully identifies cross‑organizational campaign cohorts from fragmented local data.

By Manuel R\"oder, Bibin Babu, Frank-Michael Schleif

rag benchmarks

arXiv Machine Learning

Aug 4

DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment

arXiv:2511. 13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions.

By Ci Lin, Tet Yeap, Iluju Kiringa

safety

arXiv Machine Learning

Sep 10

Fine-grained Distributed Backdoor Attacks in Federated Learning

arXiv:2609.07147v1 Announce Type: new Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to central...

By Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu

rag safety

arXiv AI

Aug 7

DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning

arXiv:2511. 04949v2 Announce Type: replace-cross Abstract: Rapid advances in generative AI have led to increasingly realistic deepfakes, posing growing challenges for law enforcement and public trust.

By Tharindu Fernando, Clinton Fookes, Sridha Sridharan

agents diffusion reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 30

Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization

arXiv:2511. 07210v3 Announce Type: replace-cross Abstract: Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat to security-critical applications.

By Binyan Xu, Fan Yang, Di Tang, Xilin Dai, Kehuan Zhang

computer-vision robotics

One email a morning, machine-written

Related stories

TOFD: Target-Oriented Feature Decoupling against Poisoning Attacks in Split Federated Learning

Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment

Fine-grained Distributed Backdoor Attacks in Federated Learning

DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning

Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization