A Review of Deepfake Techniques: Architecture, Detection, and Datasets

Driven by continuous advancements in artificial intelligence, especially deep learning, the level of realism associated with deepfake technology continues to improve year after year, which poses unprecedented challenges to the field of deepfake detection. The boundary between what we as humans can detect as real or fake becomes evermore blurred as new generations of algorithms such as Dall-E 3 and Stable Diffusion are released. This paper provides a comprehensive study into the landscape of deepfake detection, exploring in-depth the key challenges, recognising recent successes, and suggesting promising avenues for future research. A meta-literature review is conducted to identify the current challenges and future directions, which form the foundation of this work. They are investigated by analysing state-of-the-art research with a focus on the key components that are crucial to the design of a deepfake detector, i.e., the architecture, detection methods and datasets. A major challenge identified by this study is the lack of dataset diversity leading to unfair attribute representation. This must be addressed by improving standardisation on dataset ethics and privacy. This is one of the main reasons for the insufficient generalisation capability of current deepfake detectors as demonstrated by their unsatisfactory performance when faced with unseen data or data in the wild. This literature review provides deepfake detection researchers and practitioners with the latest information that will serve as a vital resource for their continued and important activity, now and in the future.