Towards Interactive Deepfake Analysis

• URL: http://arxiv.org/abs/2501.01164v1
• Date: Thu, 02 Jan 2025 09:34:11 GMT
• Title: Towards Interactive Deepfake Analysis
• Authors: Lixiong Qin, Ning Jiang, Yang Zhang, Yuhan Qiu, Dingheng Zeng, Jiani Hu, Weihong Deng,
• Abstract summary: This paper aims to explore interactive deepfake analysis by performing instruction tuning on multi-modal large language models (MLLMs) To address these issues, we introduce (1) a GPT-assisted data construction process resulting in an instruction-following dataset called DFA-Instruct, (2) a benchmark named DFA-Bench, designed to comprehensively evaluate the capabilities of MLLMs in deepfake detection, deepfake classification, and artifact description, and (3) construct an interactive deepfake analysis system called DFA-GPT, as a strong baseline for the community.
• Score: 40.0271474912034
• License:
• Abstract: Existing deepfake analysis methods are primarily based on discriminative models, which significantly limit their application scenarios. This paper aims to explore interactive deepfake analysis by performing instruction tuning on multi-modal large language models (MLLMs). This will face challenges such as the lack of datasets and benchmarks, and low training efficiency. To address these issues, we introduce (1) a GPT-assisted data construction process resulting in an instruction-following dataset called DFA-Instruct, (2) a benchmark named DFA-Bench, designed to comprehensively evaluate the capabilities of MLLMs in deepfake detection, deepfake classification, and artifact description, and (3) construct an interactive deepfake analysis system called DFA-GPT, as a strong baseline for the community, with the Low-Rank Adaptation (LoRA) module. The dataset and code will be made available at https://github.com/lxq1000/DFA-Instruct to facilitate further research.

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