Image Detection Using Convolutional Neural Networks: A Review of Transfer Learning, Ensemble Learning and Gated Expert Models for AI-Generated Image Detection
Keywords:
AI-generated images, Convolutional neural network, Deep learning, Ensemble learning, Gated network, Image classification, Image detection, Transfer learningAbstract
Image detection and classification are the core problems in computer vision, as demonstrated by the emergence of Convolutional Neural Networks (CNNs), which enable learning visual representations directly from images without requiring manual engineering of descriptors. Consequently, CNNs can be leveraged to solve practical image recognition tasks, including the detection of synthetic and AI-generated content with a high degree of accuracy. This review article provides an overview of the image detection problems, focusing on the CNN-based approach. The article reviews the CNN developments and outlines the transfer learning approach that was employed to fine-tune the pre-trained ResNet-50 convolutional network for the task of image detection. Additionally, the study discusses the approaches that employ ensembling, such as averaging, voting, unified training, gated mixture-of-experts, and the application of multiple CNNs to solve the detection problem stated. Finally, the review compares the findings with other related works, noting the trends that reveal the difficulties of developing a reliable and general image detection method, particularly in terms of performance differences across different types of generators. The computational costs and limitations of the current approaches are also described alongside potential research directions, which include the development of larger and more comprehensive benchmark data sets for training and testing detection algorithms. The study concludes by stating that while CNNs are an effective tool for image detection due to their capacity to represent high-level concepts with relatively low computational cost, the generalization ability of current methods is often insufficient to address real-world problems, and thus ongoing research efforts are required.
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