Advance deep learning techniques for deepfake detection
Indresh Goswami, Mayank Sisodia, Ajeet Singh · 2024
The need for a reliable method to identify complex facial deepfakes has grown more urgent in a time when deepfake content is widely disseminated on social media and sophisticated technologies are widely used. The authentication of visual material is becoming difficult as deepfake manipulation tools develop. With an emphasis on collecting a wider range of face traits, this study presents a novel framework for the identification of deepfakes using deep learning techniques. In the first step, additional preprocessing methods are used in addition to scaling the picture to match the input layers of the convolutional neural network. Error-level analysis of image to generate pixel level analysis. These include super-resolution models and noise reduction techniques, which improve the clarity and detail of the incoming data enabling a more thorough analysis. By combining ensemble models and transfer learning, the deep feature extraction phase is greatly improved. A more thorough and precise knowledge of complex face characteristics is ensured by using pre-trained CNN models as feature extractors in conjunction with a diversified ensemble of CNN architectures. Then deep learning classifiers are used that are capable of sequential feature processing, in order to efficiently classify the retrieved features. This multidimensional method places special focus on collecting a broad range of facial traits in an effort to offer a comprehensive solution for the identification of deepfake material. The suggested system shows its effectiveness and reliability, with a focus on reaching a greater level of deepfake detection accuracy.