Deep fake technology has gained significant attention in recent years due to its potential to generate highly realistic counterfeit images and videos, raising concerns about the integrity of visual media. Addressing the challenge of detecting deep fake images is crucial to ensure trustworthiness in various domains, including journalism, forensics, and social media platforms. This project aims to explore the effectiveness of frequency analysis techniques and deep learning for deep fake image recognition, offering a comprehensive study to enhance the reliability of detection methods.

The project utilizes a frequency analysis technique DCT, to extract spectral features that capture unique characteristics of deep fake images. These features are then used to train deep learning models, enhancing their ability to accurately classify deep fake images. We also aim to build a frontend web app which identifies a deep fake image and if possible where one can upload an image and it will classify the image as deep fake or real. We use Streamlit for the frontend display of the web-app.By empowering users to detect deep fake images and promoting transparency, the project contributes to combating the harmful effects of deepfakes and fostering a more trustworthy digital media environment.

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FFHQ-dataset has been used for real images. Fake images have been generated via the use of StyleGAN

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1. Reference
2. https://github.com/RUB-SysSec/GANDCTAnalysis

1. K V Srinanda
2. Charu Shah
3. Vishal Marwade {github id}

1. Aniket Kulkarni
2. Aryan N. Herur
3. Jobin Jacob
4. Vaibhav Santhosh