Formal Verification of Deep Neural Networks for Object Detection
This addresses the need for systematic verification of object detection models to enhance robustness and reliability, though it is incremental as it adapts existing methods to a new domain.
This work tackled the problem of extending formal verification to object detection models, which are more complex than image classification models, and demonstrated that state-of-the-art verification tools can be adapted to uncover vulnerabilities in these models.
Deep neural networks (DNNs) are widely used in real-world applications, yet they remain vulnerable to errors and adversarial attacks. Formal verification offers a systematic approach to identify and mitigate these vulnerabilities, enhancing model robustness and reliability. While most existing verification methods focus on image classification models, this work extends formal verification to the more complex domain of emph{object detection} models. We propose a formulation for verifying the robustness of such models and demonstrate how state-of-the-art verification tools, originally developed for classification, can be adapted for this purpose. Our experiments, conducted on various datasets and networks, highlight the ability of formal verification to uncover vulnerabilities in object detection models, underscoring the need to extend verification efforts to this domain. This work lays the foundation for further research into formal verification across a broader range of computer vision applications.