arXiv:2505.08814v3 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) play a crucial role in the field of artificial intelligence, and their security-related testing has been a prominent research focus. By inputting test cases, the behavior of models is examined for anomalies, and coverage metrics are utilized to determine the extent of neurons covered by these test cases. With the widespread application and advancement of DNNs, different types of neural behaviors have garnered attention, leading to the emergence of various coverage metrics for neural networks. However, there i
The proliferation of deep neural networks necessitates better testing and understanding for security and reliability, driving new research in coverage metrics.
Improved methods for understanding and testing deep learning models will enhance their reliability, security, and trustworthiness in critical applications.
The focus on coverage testing for deep neural networks will lead to more robust AI systems, potentially influencing industry standards for AI deployment.
- · AI developers
- · Cybersecurity firms
- · Regulators
- · Industries deploying AI
- · Malicious actors exploiting AI vulnerabilities
- · Companies with unreliable AI systems
Enhanced understanding of deep learning behavior informs more secure and dependable AI deployments.
This improved reliability could accelerate the adoption of AI in sensitive sectors like defense or finance.
Standardization of AI testing methodologies based on coverage metrics could emerge as a critical industry benchmark.
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