A Hybrid Approach to Deep Fake Detection Using Error Level Analysis

Abstract

The rapid advancement of ‘deepfake’ video technology— which uses deep learning artificial intelligence algorithms to create fake videos that look real—has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people’s alertness to and ability to detect a high-quality deepfake among a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compared to a control group who viewed only authentic videos (34.1%). Second, we find that when individuals are given a warning that at least one video in a set of five is a deepfake, only 21.6% of respondents correctly identify the deepfake as the only inauthentic video, while the remainder erroneously select at least one genuine video as a deepfake. The process of recognizing and distinguishing between real content and content generated by deep learning algo rhythm’s, often referred to as deepfakes, is known as deepfake detection. In order to counter the rising threat of deepfakes and maintain the integrity of digital media, research is now being done to create more reliable and precise detection techniques. Deep learning models, such as Stable Diffusion, have been able to generate more detailed and less blurry images in recent years. In this paper, we develop a deepfake detection technique to distinguish original and fake images generated by various Diffusion Models. The developed methodology for deepfake detection takes advantage of features from fine-tuned Vision Transformers (ViTs), combined with existing classifiers such as Support Vector Machines (SVM). We demonstrate the proposed methodology’s ability of interpretability-through-prototypes by analysing support vectors of the SVMs