Nearest Neighbor Projection Removal Adversarial Training

Summary

The authors introduce Nearest Neighbor Projection Removal Adversarial Training, a framework that removes projections onto each adversarial sample's nearest inter-class neighbors in feature space to improve class separability. They report theoretically that their logits correction lowers the Lipschitz constant and Rademacher complexity, and experiments on CIFAR-10, CIFAR-100, SVHN and TinyImagenet show performance competitive with leading adversarial training methods.

Related items

• InfoAnchored Rank-Adversarial Fine-Tuning Pretrained Visual Place Recognition ModelsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoSay the Same, Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoDetect, Repair and Audit: A Hierarchical Multi-Agent Defense Against Adversarial Attacks in Energy ForecastingSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoSANAI: Secure ECC Distributed Keyed Parity Embedded in Weights for Protecting Neural Networks against Bit-Flip AttacksSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoTowards Fine-grained and Robust Detection of Human-AI Co-authorshipSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)