A knowledge distillation based deep learning framework for cropped images detection in spatial domain

Cropping an image is a common image editing technique that aims to find viewpoints with suitable image composition. It is also a frequently used post-processing technique to reduce the evidence of tampering in an image. Detecting cropped images poses a significant challenge in the field of digital image forensics, as the distortions introduced by image cropping are often imperceptible to the human eye. Although deep neural networks achieve state-of-the-art performance, due to their ability to encode large-scale data and handle billions of model parameters. However, due to their high computational complexity and substantial storage requirements, it is difficult to deploy these large deep learning models on resource-constrained devices such as mobile phones and embedded systems. To address this issue, we propose a lightweight deep learning framework for cropping detection in spatial domain, based on knowledge distillation. Initially, we constructed four datasets containing a total of 60,000 images cropped using various tools. We then used Efficient-Net-B0, pre-trained on ImageNet with significant surgical adjustments, as the teacher model, which makes it more robust and faster to converge in this downstream task. The model was trained on 20,000 cropped and uncropped images from our own dataset, and we then applied its knowledge to a more compact model called the student model. Finally, we selected the best-performing lightweight model as the final prediction model, with a testing accuracy of 98.44% on the test dataset, which outperforms other methods. Extensive experiments demonstrate that our proposed model, distilled from Efficient-Net-B0, achieves state-of-the-art performance in terms of detection accuracy, training parameters, and FLOPs, outperforming existing methods in detecting cropped images.