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- W4284961122 abstract "Abstract The advancements in Image editing techniques produce realistic-looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth-wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The nor-malization layers also help stabilize training whenever the spatial resolution is changed. The depth-wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state-of-the-art methods by a large margin." @default.
- W4284961122 created "2022-07-10" @default.
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- W4284961122 date "2022-07-09" @default.
- W4284961122 modified "2023-10-14" @default.
- W4284961122 title "ForensicNet : Modern CNN-based Image Forgery Detection Network" @default.
- W4284961122 doi "https://doi.org/10.21203/rs.3.rs-1802559/v1" @default.
- W4284961122 hasPublicationYear "2022" @default.
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