A Review of Application of Reinforcement Learning for Detection of Deepfake Images

Abstract

Recent developments prove that reinforcement learning (RL) and deep learning are becoming increasingly central to deepfake image detection as a response to the developing complexity of synthetic media. RL improves model flexibility, enabling dynamic adaptations to developing manipulation strategies and adversarial attacks. Hybrid solutions combining RL with architectures such as ResNet, CNNs, and LSTMs prove promising accuracy by being able to capture spatial and temporal information. For instance, RL-driven pipelines can optimize feature extraction, reward manipulation-artifact detection, and improve prediction logic through trial-and-error learning, thereby enhancing detection robustness. Although RL can enhance generalization and resistance against various deepfake techniques and datasets, significant challenges persist, including extensive computational demands, dependency on large labeled datasets, and difficulties in interpreting “black box” model decisions. In spite of these constraints, RL-based approaches with interpretable AI methods and multi-domain feature fusion are becoming scalable, effective tools to counter online disinformation and safeguard media authenticity. Future work should be centered on building lightweight, interpretable RL-based models that can robustly identify deepfake images in varied real-world scenarios.