How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception
Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao and, Hsin-Min Wang

TL;DR
This study evaluates ChatGPT's ability to detect audiovisual deepfakes, comparing its performance with state-of-the-art models and humans, highlighting the importance of domain knowledge and prompt engineering.
Contribution
It demonstrates ChatGPT's potential in audiovisual forgery detection and discusses its limitations, offering insights into prompt-based detection methods versus traditional models.
Findings
ChatGPT can identify spatial and spatiotemporal artifacts in deepfakes.
Prompt engineering significantly impacts detection performance.
ChatGPT's detection capabilities are comparable to or better than some multimodal models.
Abstract
Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learningbased forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · COVID-19 diagnosis using AI · Explainable Artificial Intelligence (XAI)
