Deep feature extraction for document forgery detection with convolutional autoencoders

Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2∼5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above – mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images.