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Table 1 Network architecture of proposed model

From: The secure steganography for hiding images via GAN

Encoder Decoder Discriminator (Xu’Net)
Input:6×256×256 Input:3×256×256 Input:3×256×256
Conv(6, 64, 7)   
Conv(64,128,3)   
Conv(128,256,3)   
Res_block1(256,256,3) Conv(3, 64,3) kv_filter(3,1,5)
Res_block2(256,256,3) Conv(64, 128,3) Conv(1, 8, 5)
Res_block3(256,256,3) Conv(128,256,3) Conv(8, 16, 5)
Res_block4(256,256,3) Conv(256,128,3) Conv(16, 32, 3)
Res_block5(256,256,3) Conv(128, 64, 3) Conv(32, 64,3)
Res_block6(256,256,3) Conv(64, 3,3) Conv(64,128,3)
Res_block7(256,256,3) Sigmoid(3, 3) Sigmoid(128,1)
Res_block8(256,256,3)   
Res_block9(256,256,3)   
DeConv(256,128, 3)   
DeConv(128, 64, 3)   
Conv(64, 3, 7)   
Tanh(3,3)   
Output:3×256×256 Output:3×256×256 Output:1
  1. Each resnet block includes two 3×3 convolutional layers with batch normalization operation and ReLU activation function, which outputs the result of adding the input feature map and the feature map after two convolutional layers