Optimizing AIGC Image Detection: Strategies in Data Augmentation and Model Architecture

With the development of AIGC, images generated by AI are difficult to identify with the human eye. This article proposes a simple training architecture and a simple model. In the training process of deep forgery detection, a variety of data enhancement techniques are used, including horizontal flipping, adding Gaussian noise and random cropping to enhance the generalization ability of the model. Differentiated processing strategies are adopted for images of different sizes: center cropping is used for images smaller than 224 pixels, random cropping is used for images between 224 and 512 pixels, and images larger than 512 pixels are first scaled and then randomly cropped. Furthermore, the concept of neighbor pixel relations (NPR) is introduced, which is an effective way to capture local structural artifacts introduced by upsampling operations. As a complement to image representation, NPR significantly improves the detector's ability to identify forged images, especially when the forgery technology is unknown. By integrating the NPR module into the ResNet-50 model, it not only enhances the classifier's extraction of forged features, but also maintains the computational efficiency and versatility of the model. Finally, AIGC generated images in the ''Malanshan Cup" International Audio-Video Algorithm Competition. Determine the first place result for the track.