#neural-network-security News & Analysis

3 articles tagged with #neural-network-security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBearisharXiv – CS AI · Jun 257/10

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Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation

Researchers demonstrate that Physics-Informed Neural Networks (PINNs) can achieve low training loss while producing wildly inaccurate solutions when underlying PDE parameters are corrupted, revealing a critical gap between loss minimization and physical correctness. The study proposes a post-hoc defense mechanism that sweeps residual loss across parameter values to recover true parameters without retraining, offering a practical solution across multiple PDE systems and network architectures.

AINeutralarXiv – CS AI · Jun 116/10

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Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

Researchers demonstrate that reinforcement learning (RL) can disrupt gradient-based adversarial attacks on deep neural networks by creating unstable gradient structures, and when combined with adversarial training, provides dual-layer defense that significantly outperforms traditional supervised learning approaches across multiple attack types.

AINeutralarXiv – CS AI · Apr 146/10

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The Weight of a Bit: EMFI Sensitivity Analysis of Embedded Deep Learning Models

Researchers demonstrate that embedded neural network models using integer representations (8-bit and 4-bit) are significantly more resilient to electromagnetic fault injection attacks than floating-point formats (32-bit and 16-bit). The study reveals that floating-point models experience near-complete accuracy degradation from a single fault, while 8-bit integer representations maintain robust performance, with implications for securing AI systems deployed on edge devices.