Authors: Chuangchuang Tan

Affiliation: Beijing Jiaotong University

Email: chuangchuangtan@aliyun.com

Description: deepfakedetection

Authors: Davide Alessandro Coccomini, Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro

Affiliation: ISTI-CNR

Email: davidealessandro.coccomini@isti.cnr.it

Description: We fine-tuned a Swin Transformer Base pre-trained on Imagenet on the provided training set. The training images underwent heavy random data augmentation in the training phase (inspired by [1]) to spur the models to generalize better. Since the images generated by Diffusion Models are known to introduce noise, the models could be made to overfit by learning to recognize it exclusively. To avoid this, among the various transformations applied to images, there are many noise addition and compression techniques, even in combination. Also, some random rotation, brightness, crops, dropouts, resize and many other manipulations are applied to boost generalization.
During the training process the images are also transformed in the DCT domain since with a probability of 50% since, as shown in [2], this should emphasize the artifacts.

In order to choose the best model we also created a custom Validation Set composed of real images taken from Flickr Dataset and images generated by GANs (ProGAN, StyleGAN, StyleGAN2 and RelGAN) and with Diffusion Models (Stable Diffusion and GLIDE) inspired by "Detecting Images generated by Diffusers".

Authors: Davide Alessandro Coccomini, Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro

Description: We fine-tuned two Deep Learning models pretrained on Imagenet. Specifically a two Swin Transformer Base. The images underwent heavy random data augmentation in the training phase (inspired by [1]) to spur the models to generalize better. Since the images generated by Diffusion Models are known to introduce noise, the models could be made to overfit by learning to recognize it exclusively. To avoid this, among the various transformations applied to images, there are many noise addition and compression techniques, even in combination. Also, some random rotation, brightness, crops, dropouts, resize and many other manipulations are applied to boost generalization.
During the training process of one of the two Swin Transformers, the images are also transformed in the DCT domain since with a probability of 50% since, as shown in [1], this should emphasize the artifacts.
Both the models are used to make a prediction on each image in the test set and the final prediction is the mean of the two predictions.

In order to choose the best model we also created a custom Validation Set composed of real images taken from Flickr Dataset and images generated by GANs (ProGAN, StyleGAN, StyleGAN2 and RelGAN) and with Diffusion Models (Stable Diffusion and GLIDE) inspired by "Detecting Images generated by Diffusers".