Synthetic Art Generation and DeepFake Detection A Study on Jamini Roy Inspired Dataset

• URL: http://arxiv.org/abs/2503.23226v1
• Date: Sat, 29 Mar 2025 21:12:16 GMT
• Title: Synthetic Art Generation and DeepFake Detection A Study on Jamini Roy Inspired Dataset
• Authors: Kushal Agrawal, Romi Banerjee,
• Abstract summary: This study takes a unique approach by examining diffusion-based generative models in the context of Indian art.<n>To explore this, we fine-tuned Stable Diffusion 3 and used techniques like ControlNet and IPAdapter to generate realistic images.<n>We employed various qualitative and quantitative methods, such as Fourier domain assessments and autocorrelation metrics, to uncover subtle differences between synthetic images and authentic pieces.
• Score: 1.0742675209112622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The intersection of generative AI and art is a fascinating area that brings both exciting opportunities and significant challenges, especially when it comes to identifying synthetic artworks. This study takes a unique approach by examining diffusion-based generative models in the context of Indian art, specifically focusing on the distinctive style of Jamini Roy. To explore this, we fine-tuned Stable Diffusion 3 and used techniques like ControlNet and IPAdapter to generate realistic images. This allowed us to create a new dataset that includes both real and AI-generated artworks, which is essential for a detailed analysis of what these models can produce. We employed various qualitative and quantitative methods, such as Fourier domain assessments and autocorrelation metrics, to uncover subtle differences between synthetic images and authentic pieces. A key takeaway from recent research is that existing methods for detecting deepfakes face considerable challenges, especially when the deepfakes are of high quality and tailored to specific cultural contexts. This highlights a critical gap in current detection technologies, particularly in light of the challenges identified above, where high-quality and culturally specific deepfakes are difficult to detect. This work not only sheds light on the increasing complexity of generative models but also sets a crucial foundation for future research aimed at effective detection of synthetic art.

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