FIDAVL: Fake Image Detection and Attribution using Vision-Language Model

• URL: http://arxiv.org/abs/2409.03109v1
• Date: Thu, 22 Aug 2024 15:41:56 GMT
• Title: FIDAVL: Fake Image Detection and Attribution using Vision-Language Model
• Authors: Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene, Abdelmalik Taleb-Ahmed, Abdenour Hadid,
• Abstract summary: FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. It exploits the complementarity between vision and language along with soft prompt-tuning strategy to detect fake images. FIDAVL achieves an encouraging average detection accuracy of 95.42% and F1-score of 95.47%.
• Score: 14.448350657613368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. Leveraging the benefits of zero-shot learning, FIDAVL exploits the complementarity between vision and language along with soft prompt-tuning strategy to detect fake images and accurately attribute them to their originating source models. We conducted extensive experiments on a comprehensive dataset comprising synthetic images generated by various state-of-the-art models. Our results demonstrate that FIDAVL achieves an encouraging average detection accuracy of 95.42% and F1-score of 95.47% while also obtaining noteworthy performance metrics, with an average F1-score of 92.64% and ROUGE-L score of 96.50% for attributing synthetic images to their respective source generation models. The source code of this work will be publicly released at https://github.com/Mamadou-Keita/FIDAVL.

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