Accepted: 16 May 2024 / Published online: 29 May 2024 | Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna, Selena Firmin, Feng Xia
The paper "Deepfake Video Detection: Challenges and Opportunities" by Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna, Selena Firmin, and Feng Xia, published in May 2024, addresses the growing social issue of deepfake videos. These videos are created using AI techniques, particularly deep learning, and are often misused to spread false information. The authors analyze the challenges in detecting deepfakes, including data unbalance, inadequate labeled training data, high computational requirements, and emerging manipulation methods. They highlight the dominance of deep learning-based methods despite their limitations in computational efficiency and generalization. The paper also emphasizes the need for high-quality datasets to improve detection methods and identifies major research gaps, particularly in developing robust models for real-time detection. The introduction provides a historical context, explaining how deepfakes have evolved from simple image editing to sophisticated video manipulations, and discusses the increasing prevalence of deepfakes in social media, with a focus on visual deepfakes.The paper "Deepfake Video Detection: Challenges and Opportunities" by Achhardeep Kaur, Azadeh Noori Hoshyar, Vidya Saikrishna, Selena Firmin, and Feng Xia, published in May 2024, addresses the growing social issue of deepfake videos. These videos are created using AI techniques, particularly deep learning, and are often misused to spread false information. The authors analyze the challenges in detecting deepfakes, including data unbalance, inadequate labeled training data, high computational requirements, and emerging manipulation methods. They highlight the dominance of deep learning-based methods despite their limitations in computational efficiency and generalization. The paper also emphasizes the need for high-quality datasets to improve detection methods and identifies major research gaps, particularly in developing robust models for real-time detection. The introduction provides a historical context, explaining how deepfakes have evolved from simple image editing to sophisticated video manipulations, and discusses the increasing prevalence of deepfakes in social media, with a focus on visual deepfakes.