CNN-generated images are surprisingly easy to spot... for now

In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark)... (read more)

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Methods used in the Paper


METHOD TYPE
PatchGAN
Discriminators
Tanh Activation
Activation Functions
Instance Normalization
Normalization
Sigmoid Activation
Activation Functions
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
CycleGAN
Generative Models
Softmax
Output Functions
ReLU
Activation Functions
Conditional Batch Normalization
Normalization
Residual Block
Skip Connection Blocks
TTUR
Optimization
GAN Hinge Loss
Loss Functions
Residual Connection
Skip Connections
Non-Local Operation
Image Feature Extractors
Non-Local Block
Image Model Blocks
Truncation Trick
Latent Variable Sampling
Linear Layer
Feedforward Networks
Dot-Product Attention
Attention Mechanisms
Projection Discriminator
Discriminators
Spectral Normalization
Normalization
Off-Diagonal Orthogonal Regularization
Regularization
Adam
Stochastic Optimization
Batch Normalization
Normalization
Early Stopping
Regularization
1x1 Convolution
Convolutions
SAGAN Self-Attention Module
Attention Modules
SAGAN
Generative Adversarial Networks
BigGAN
Generative Models
Convolution
Convolutions
Adaptive Instance Normalization
Normalization
R1 Regularization
Regularization
Leaky ReLU
Activation Functions
Dense Connections
Feedforward Networks
Feedforward Network
Feedforward Networks
StyleGAN
Generative Models