FIDAVL: Fake Image Detection and Attribution using Vision-Language Model
<|reference_start|>FIDAVL: Fake Image Detection and Attribution using Vision-Language Model: 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.<|reference_end|>
@article{keita2024fidavl:,
    title={FIDAVL: Fake Image Detection and Attribution using Vision-Language Model},
    author={Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene,
  Abdelmalik Taleb-Ahmed, Abdenour Hadid},
    journal={arXiv preprint arXiv:2409.03109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03109},
    primaryClass={cs.CV cs.CR}
}