<ul><li>Deep neural networks (DNNs) are difficult to understand and explain, making them challenging to defend.</li><li>A novel approach is proposed to extract critical paths from DNNs and utilize them for anomaly detection.</li><li>By identifying critical detection paths through genetic evolution and mutation, the method integrates multiple paths' results for accurate anomaly detection.</li><li>Experimental results indicate that this new approach surpasses existing methods and is effective in detecting various types of anomalies with high precision.</li></ul>

Anomaly Detection Based on Critical Paths for Deep Neural Networks

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