Detective AI: distinguishing AI generated and real images by leveraging the concept of cross-correlation of connected image components of bit-planes

Shan Suthaharan

doi:10.1117/12.3027551

4 October 2024 Detective AI: distinguishing AI generated and real images by leveraging the concept of cross-correlation of connected image components of bit-planes

Shan Suthaharan

Author Affiliations +

Proceedings Volume 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; 131180A (2024) https://doi.org/10.1117/12.3027551
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

The rapidly emerging Generative AI technology that generates artificial images of real objects demands the development of a Detective AI technology to offer an efficient solution to solve the problem of distinguishing real and AI-generated images with high accuracy. The Generative AI, such as the Generative Adversarial Networks (e.g., BigGAN), can generate images that in turn can trick the human visual system and confuse human intelligence from seeing the true differences between AI-generated and real images. The Generative AI models, while capable of generating realistic images by focusing on image features, they lack efficiency in capturing complex optical features, including reflection, refraction, and shadows, that can leave traces and clues in the 8 bit-planes of a grayscale image. This paper proposes an approach that utilizes topological properties of the bit-plane images. It detects image components and characterizes their connectivity and adjacency relationships. The feature vectors that are generated using cross-correlation of these connected components in the bit-planes are used. A feature space that is constructed using these feature vectors is utilized to train and develop random forest (RF) classifiers for classifying AI-generated and real images. In a simulation with 200 BigGAN-generated images and 248 real images of house-finch birds, a random forest classifier is developed and validated. The results show, with a careful tuning of the RF parameters, the BigGAN-generated images and their real images can be classified with the F1-score and precision-score of about 81% and 82%, respectively. This research suggests that the cross-correlation features of the connected components can help distinguish real and AI-generated images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shan Suthaharan "Detective AI: distinguishing AI generated and real images by leveraging the concept of cross-correlation of connected image components of bit-planes", Proc. SPIE 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, 131180A (4 October 2024); https://doi.org/10.1117/12.3027551

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