A Hybrid GAN-CNN Network with Attention Mechanism for Detecting Fake Profile Images in Microblogs

Fake identities on social networks and microblogs arise from the creation of fraudulent account photographs and cyberattacks, enabling malicious actors to gain access to identification systems and compromise accounts. The objective of this study was to develop a neural network for detecting fake images, representing a symbiosis of Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN). The proposed neural network, named GAN-CNN with Attention Mechanism Network (GAMN), integrates an attention mechanism to enhance detection accuracy. To describe the texture of images, a Gram matrix was employed. The contrast of the image textures was analyzed by calculating the Pearson correlation coefficient between the original and edited images. For the evaluation of test images, the “ResNet” model of the CNN was utilized alongside the developed neural network “GAMN.” The Class Activation Map (CAM) method was applied to identify differences between fake and authentic faces. An attention mechanism was integrated into the convolutional neural network by adding a self-attention layer. This enabled the model to assign varying importance to the different image parts (such as eyes, mouth, and background) based on the likelihood of alterations. The activation of the attention improved the neural network’s prediction accuracy from 80.74% to 89.27%. The performance of the developed “GAMN” neural network outperforms the “ResNet” detector by 10% and the Co-detect detector by 30%.