Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro Data sets Model Results Discussion, Acknowledgements, and References 3. Model We train a model to distinguish real from AI-generated faces. The underlying model is the EfficientNet-B1[7] convolutional neural network [30]. We found that this architecture provides better performance as compared to other stateof-the-art architectures (Swin-T [22], Resnet50 [14], XceptionNet [7]). The EfficientNet-B1 network has 7.8 million internal parameters that were pre-trained on the ImageNet1K image dataset [30]. Our pipeline consists of three stages: (1) an image preprocessing stage; (2) an image embedding stage; and (3) a scoring stage. The model takes as input a color image and generates a numerical score in the range [0, 1]. Scores near 0 indicate that the image is likely real, and scores near 1 indicate that the image is likely AI-generated. The image pre-processing step resizes the input image to a resolution of 512×512 pixels. This resized color image is then passed to an EfficientNet-B1 transfer learning layer. In the scoring stage, the output of the transfer learning layer is fed to two fully connected layers, each of size 2,048, with a ReLU activation function, a dropout layer with a 0.8 dropout probability, and a final scoring layer with a sigmoidal activation. Only the scoring layers – with 6.8 million trainable parameters – are tuned. The trainable weights are optimized using the AdaGrad algorithm with a minibatch of size 32, a learning rate of 0.0001, and trained for up to 10,000 steps. A cluster with 60 NVIDIA A100 GPUs was used for model training. This paper is available on arxiv under CC 4.0 license. [7] We are describing an older version of the EfficientNet model which we have previously operationalized on the LinkedIn that has since been replaced with a new model. We recognize that this model is not the most recent, but we are only now able to report these results since the model is no longer in use. Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Authors: Authors: (1) Gonzalo J. Aniano Porcile, LinkedIn; (2) Jack Gindi, LinkedIn; (3) Shivansh Mundra, LinkedIn; (4) James R. Verbus, LinkedIn; (5) Hany Farid, LinkedIn and University of California, Berkeley. Table of Links Abstract and Intro Data sets Model Results Discussion, Acknowledgements, and References Abstract and Intro Abstract and Intro Data sets Data sets Model Model Results Results Discussion, Acknowledgements, and References Discussion, Acknowledgements, and References 3. Model We train a model to distinguish real from AI-generated faces. The underlying model is the EfficientNet-B1[7] convolutional neural network [30]. We found that this architecture provides better performance as compared to other stateof-the-art architectures (Swin-T [22], Resnet50 [14], XceptionNet [7]). The EfficientNet-B1 network has 7.8 million internal parameters that were pre-trained on the ImageNet1K image dataset [30]. Our pipeline consists of three stages: (1) an image preprocessing stage; (2) an image embedding stage; and (3) a scoring stage. The model takes as input a color image and generates a numerical score in the range [0, 1]. Scores near 0 indicate that the image is likely real, and scores near 1 indicate that the image is likely AI-generated. The image pre-processing step resizes the input image to a resolution of 512×512 pixels. This resized color image is then passed to an EfficientNet-B1 transfer learning layer. In the scoring stage, the output of the transfer learning layer is fed to two fully connected layers, each of size 2,048, with a ReLU activation function, a dropout layer with a 0.8 dropout probability, and a final scoring layer with a sigmoidal activation. Only the scoring layers – with 6.8 million trainable parameters – are tuned. The trainable weights are optimized using the AdaGrad algorithm with a minibatch of size 32, a learning rate of 0.0001, and trained for up to 10,000 steps. A cluster with 60 NVIDIA A100 GPUs was used for model training. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [7] We are describing an older version of the EfficientNet model which we have previously operationalized on the LinkedIn that has since been replaced with a new model. We recognize that this model is not the most recent, but we are only now able to report these results since the model is no longer in use.
