Automated deepfake generation process based on AI painting technology - Liu, Qianru; Kim, Ki-Hong (Edelweiss Applied Science and Technology, 2025)
This paper addresses the significant data acquisition challenges in Deepfake technology by proposing an innovative methodology leveraging AI painting generation for creating facial datasets. The study aims to overcome traditional Deepfake limitations, including privacy concerns, copyright issues, and high costs associated with acquiring authentic portrait data. The research integrates AI art generation software (Midjourney with InsightFaceSwap) with Deepfake production software (DeepFace Lab) to create an automated workflow that generates diverse, consistent facial datasets for model training. Results demonstrate that AI-generated facial datasets can effectively substitute authentic human images while maintaining high-quality outputs. The proposed workflow significantly reduces data acquisition bottlenecks, mitigates legal risks, and substantially lowers production costs. Combining AI art generation with Deepfake technology offers a promising direction for advancing synthetic media creation while balancing innovation with ethical considerations. This methodology enhances the ethical application of Deepfake technology across entertainment, education, and digital media production, potentially transforming how synthetic visual content is created and used.