arXiv:1903.12519
공식 코드 · Papers with Code
A Provable Defense for Deep Residual Networks
원문 초록
We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100. Our approach is based on differentiable abstract interpretation and introduces two novel concepts: (i) abstract layers for fine-tuning the precision and scalability of the abstraction, (ii) a flexible domain specific language (DSL) for describing training objectives that combine abstract and concrete losses with arbitrary specifications. Our training method is implemented in the DiffAI system.
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Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual Tracking
Accidental Misalignment: Fine-Tuning Language Models Induces Unexpected Vulnerability
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models
Lightweight Defense Against Adversarial Attacks in Time Series Classification
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