Sustainable Artificial Intelligence: Assessing Performance in Detecting Fake Images

Detecting fake images is crucial because they may confuse and influence people into making bad judgments or adopting incorrect stances that might have disastrous consequences. In this study, we investigate not only the effectiveness of artificial intelligence, specifically deep learning and deep neural networks, for fake image detection but also the sustainability of these methods. The primary objective of this investigation was to determine the efficacy and sustainable application of deep learning algorithms in detecting fake images. We measured the amplitude of observable phenomena using effect sizes and random effects. Our meta-analysis of 32 relevant studies revealed a compelling effect size of 1.7337, indicating that the model's performance is robust. Despite this, some moderate heterogeneity was observed (Q-value = 65.5867; I-2 = 52.7344%). While deep learning solutions such as CNNs and GANs emerged as leaders in detecting fake images, their efficacy and sustainability were contingent on the nature of the training images and the resources consumed during training and operation. The study highlighted adversarial confrontations, the need for perpetual model revisions due to the ever-changing nature of image manipulations, and data scarcity as technical obstacles. Additionally, the sustainable deployment of these AI technologies in diverse environments was considered crucial.