An Effective Semi-Supervised Feature Extraction Model with Reduced Architectural Complexity for Image Forgery Classification

A generalized deep learning approach tracking image forgeries of any category with reduced architectural complexity, without compromising the performance is presented in this paper. A convolutional encoder-decoder architecture-based image reconstruction model is framed to extract all the pertinent information from the images. Performance comparison of similar networks constructed with varying architectural complexity led to the selection of this design. The best reconstruction feature extractor showed faster convergence and improved accuracy, as observed from the training and validation performance curves. Dimensionally compressed information from the reconstruction model is utilized by dense layers and further classified. Experimenting with forgery datasets inclusive of different forgery types ensured the generalizability of the model. In comparison with the reconstruction models adopting transfer learning in the encoder side utilizing MobileNet, ResNet 50, and VGG 19, the proposed model exhibited competitive and consistently improved mean Precision and F1-score performance across multiple datasets, as validated through multi-seed experimentation. Additionally, with the reduced architecture, the proposed model performed on par than the state-of-the-art approaches against which it was compared.