DenseNet-Based Detection of AI-Generated Driving Scene Images

Abstract

The proliferation of deepfake technologies poses a significant challenge to the integrity of image data used in autonomous driving systems, where the distinction between real and manipulated images is critical for safe and reliable operation. This study proposes a novel deepfake detection framework designed specifically for real and fake image classification in autonomous driving environments. The primary aim is to enhance the robustness of autonomous systems against adversarial manipulations by leveraging advanced deep learning techniques. The proposed model incorporates DenseNet blocks to efficiently extract hierarchical features from complex visual data, ensuring improved detection accuracy and computational efficiency. The methodology includes preprocessing the dataset, augmenting it to simulate real-world variations, and training the model on a diverse set of real and fake images. Experimental results demonstrate the efficacy of the proposed framework, achieving an impressive 98% classification accuracy, thereby underscoring its potential as a reliable solution for real-time deepfake detection in autonomous driving scenarios.