mlsanityData to 7 Oct 2026

Towards Interactive Deepfake Analysis

arXiv 2501.01164 ↗First posted 2 Jan 2025cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 40 of its 47 references that are in the map. First posted on arXiv 2 Jan 2025.

Builds on

40 of 47 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Fake-in-Facext: Towards Fine-Grained Explainable DeepFake Analysiscited by 2
  2. 2026Explainable Deepfake Detection with RL Enhanced Self-Blended Imagescited by 1

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