Research Article
Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency
Table 2
Comparison with deep learning based methods for image forensics.
| Method | Main technique | Application | Hashing |
| Chen [15] | Filter layer that output median filtering residual | Median Filtering detection | No | Qian [16] | Customized CNN model | Steganalysis | No | Bayar [17] | New convolutional layer to learn manipulation detection features | Image manipulation (i.e. median filtering, gaussian blurring, additive white Gaussian noise, eesampling) detection | No |
| Bondi [18] | Clustering of camera-based CNN features | Tampering Detection and Localization | No | Yarlagadda [19] | GAN and one-class classifier | Satellite image forgery detection and Localization | No | D’Avino [20] | Autoencoder with recurrent neural networks | Video forgery detection | No |
| Tuama [21] | A layer of preprocessing is added to the CNN model | Camera model identification | No | Bondi [22] | data-driven algorithm based on convolutional neural networks | Camera model identification | No |
| Proposed | Multiview feature, perceptual saliency, semi-supervised hashing | Image tamper detection | Yes |
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