Cryptographic Method for Secure Object Segmentation for Autonomous Driving Perception Systems

<div>The modern-day vehicle’s driverless or driver-assisted systems are developed by sensing the surroundings using a combination of camera, lidar, and other related sensors by forming an accurate perception of the driving environment. Machine learning algorithms help in forming perception and perform planning and control of the vehicle. The control of the vehicle which reflects safety depends on the accurate understanding of the surroundings by the trained machine learning models by subdividing a camera image fed into multiple segments or objects. The semantic segmentation system comes with the objective of assigning predefined class labels such as tree, road, and the like to each pixel of an image. Any security attacks on pixel classification nodes of the segmentation systems based on deep learning result in the failure of the driver assistance or autonomous vehicle safety functionalities due to a falsely formed perception. The security compromisations on the pixel classification head of the object segmentation systems result in falsely segmented pixels from the incoming camera images by corrupted pixel labels with wrong object classes for the pixels. The popular encoder–decoder-based deep learning object segmentation network is considered, which is vulnerable to these attacks in its last fully connected neural network layer. Hence, the cryptographic solution mechanism is proposed here, where the pixel classes are encrypted and signed in the classification network nodes before applying the activation functions. RSA-512 algorithm-based encryption and DSA-512 algorithm-based digital signature are used to generate the proposed cryptographic components. The added cryptographic components are verified upon segmenting the objects to ensure the segmented object information is free from described security attacks. The performance of the proposed cryptographic secure object segmentation is evaluated for the popular segmentation network called U-Net for the Cityscapes segmentation dataset with the proposed cryptographic algorithms. The performance evaluation indicates that the secure semantic segmentation is performed with satisfactory precision, recall, and F1 scores of 0.86, 0.85, and 0.85, respectively, along with the added security components.</div>