How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception
- URL: http://arxiv.org/abs/2411.09266v1
- Date: Thu, 14 Nov 2024 08:07:02 GMT
- Title: How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception
- Authors: Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao, Hsin-Min Wang,
- Abstract summary: 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.
In this study, we examine the detection capabilities of a large language model (LLM) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content.
- Score: 30.295294657519165
- License:
- 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 multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks.
Related papers
- Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights [49.81915942821647]
Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception.
Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation.
This paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
arXiv Detail & Related papers (2024-11-12T09:02:11Z) - A Multimodal Framework for Deepfake Detection [0.0]
Deepfakes, synthetic media created using AI, can convincingly alter videos and audio to misrepresent reality.
Our research addresses the critical issue of deepfakes through an innovative multimodal approach.
Our framework combines visual and auditory analyses, yielding an accuracy of 94%.
arXiv Detail & Related papers (2024-10-04T14:59:10Z) - Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization [3.9440964696313485]
In the digital age, the emergence of deepfakes and synthetic media presents a significant threat to societal and political integrity.
Deepfakes based on multi-modal manipulation, such as audio-visual, are more realistic and pose a greater threat.
We propose a novel multi-modal attention framework based on recurrent neural networks (RNNs) that leverages contextual information for audio-visual deepfake detection.
arXiv Detail & Related papers (2024-08-02T18:45:01Z) - Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models [52.04189118767758]
Generalization is a main issue for current audio deepfake detectors.
In this paper we study the potential of large-scale pre-trained models for audio deepfake detection.
arXiv Detail & Related papers (2024-05-03T15:27:11Z) - Video Relationship Detection Using Mixture of Experts [1.6574413179773761]
We introduce MoE-VRD, a novel approach to visual relationship detection utilizing a mixture of experts.
MoE-VRD identifies language triplets in the form of subject, predicate, object>s to extract relationships from visual processing.
Our experimental results demonstrate that the conditional computation capabilities and scalability of the mixture-of-experts approach lead to superior performance in visual relationship detection compared to state-of-the-art methods.
arXiv Detail & Related papers (2024-03-06T19:08:34Z) - What to Remember: Self-Adaptive Continual Learning for Audio Deepfake
Detection [53.063161380423715]
Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack types.
We propose a continual learning approach called Radian Weight Modification (RWM) for audio deepfake detection.
arXiv Detail & Related papers (2023-12-15T09:52:17Z) - AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency
for Video Deepfake Detection [32.502184301996216]
Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content.
Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection.
This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor.
arXiv Detail & Related papers (2023-11-05T18:35:03Z) - AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting
Multiple Experts for Video Deepfake Detection [53.448283629898214]
The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries.
Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality.
We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
arXiv Detail & Related papers (2023-10-19T19:01:26Z) - MIS-AVoiDD: Modality Invariant and Specific Representation for
Audio-Visual Deepfake Detection [4.659427498118277]
A novel kind of deepfakes has emerged with either audio or visual modalities manipulated.
Existing multimodal deepfake detectors are often based on the fusion of the audio and visual streams from the video.
In this paper, we tackle the problem at the representation level to aid the fusion of audio and visual streams for multimodal deepfake detection.
arXiv Detail & Related papers (2023-10-03T17:43:24Z) - Contextual Object Detection with Multimodal Large Language Models [66.15566719178327]
We introduce a novel research problem of contextual object detection.
Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering.
We present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts.
arXiv Detail & Related papers (2023-05-29T17:50:33Z) - Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods.
Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art.
In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
arXiv Detail & Related papers (2022-02-19T03:48:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.