  Deep Neural Networks (DNNs) are ubiquitous in real-world applications, yet
they remain vulnerable to errors and adversarial attacks. This work tackles the
challenge of applying formal verification to ensure the safety of computer
vision models, extending verification beyond image classification to object
detection. We propose a general formulation for certifying the robustness of
object detection models using formal verification and outline implementation
strategies compatible with state-of-the-art verification tools. Our approach
enables the application of these tools, originally designed for verifying
classification models, to object detection. We define various attacks for
object detection, illustrating the diverse ways adversarial inputs can
compromise neural network outputs. Our experiments, conducted on several common
datasets and networks, reveal potential errors in object detection models,
highlighting system vulnerabilities and emphasizing the need for expanding
formal verification to these new domains. This work paves the way for further
research in integrating formal verification across a broader range of computer
vision applications.
