The architecture of the proposed 10-layer CNN Wu et al. [110] also proposed end-to-end DNNs in DIFDs. The study's CNNs extracted block features from DIs and computed self-correlations between blocks with extracted feature points matched to rebuild forged/masked areas using de-convolutions. In contrast to traditional approaches, which needed multiple training and parameter tuning steps followed by post-process spans, the proposed method eliminated multiple training/parameter adjustments. The study's scheme was trainable as it combined forged mask reconstructions with loss optimizations. Their experimental results showed that their proposed scheme beat other traditional DIFDs based on their matching schemes and effectively against assaults, including affine transforms, JPEG compressions, and blurs.

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... to suppress image effects and capture artefacts introduced in image tampers. The study's pre-trained CNNs extracted dense features of DIs, followed by feature fusions to explore discriminative features for classifying using SVMs. The scheme's experimental results on multiple datasets showed that their CNNs outperformed most other methods. Fig. 4 depicts the suggested CNNs' architecture, including 8 convolutions, 2 pooling, and 1 fullyconnected layer with a bi-way softmax classifier. Patches of 128 × 128 × 3 (128 × 128 patch, 3 color channels) make up the CNN's input volume. The first and second convolution layers contain 30 kernels with a receptive field of 55, whereas the ...