| Title | A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J. |
| Journal | IEEE Signal Processing Letters |
| Volume | 27 |
| Pagination | 276—280 |
| ISSN | 1558-2361 |
| Keywords | adaptive filtering layer, afl, batch normalization operations, classification layer, CNN, convolution, convolutional neural nets, convolutional neural network, convolutional neural networks, convolutional stream, Deep Learning, discrete cosine transform domain, discrete cosine transforms, feature extraction, Forensics, frequency-domain analysis, Human Behavior, Image forensics, information forensics, Kernel, learning (artificial intelligence), main frequency range, maxout function, median filtering, median filtering forensics, median filtering manipulation, median filters, Metrics, multiscale convolutional block, multiscale feature fusion strategy, multiscale manipulation features, operational traces, pubcrawl, resilience, Resiliency, resultant features, Scalability, sensor fusion, signal classification, Softmax function, Training |
| Abstract | This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss. |
| DOI | 10.1109/LSP.2020.2966888 |
| Citation Key | zhang_deep_2020 |
A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics