Unmasking the Fake: Detection of Deepfakes using CNNs

With the rapid development of artificial intelligence, deepfake technology has become a serious threat, producing believable but fake media that endangers cybersecurity, media, and individual privacy. Deepfake technology has advanced and become more common, and its potential for use in propaganda and misinformation makes detecting such content necessary. Unfortunately, the current detection models are ineffective in tackling the sophistication and dynamism of deepfake forgeries, hence the need for a more powerful, flexible, and efficient model. This work aims to develop an effective CNN-based model that can detect deepfake images more accurately considering the development of synthetic media technology. The model that has been proposed here uses a well-selected set of original and forged images, where preprocessing steps, including resizing, normalization, and augmentation, have been incorporated to improve the model’s transferability. The CNN architecture of the presented paper will include essential convolutional layers and advanced max-pooling layers to extract as many features as possible, as well as dense layers for binary classification. Binary cross entropy was used as the loss function, while dropout layers were used to prevent overfitting and improve the solution’s applicability in real-life situations. The model provided impressive performance across training, validation, and unseen sets. Thus, it generalizes to the variety of deepfake manipulations. These outcomes corroborate the possibilities of using CNNs as a somewhat effective tool for identifying synthetic media. This work discusses a general and extensible detection framework that plays a small but significant part in protecting the integrity of digital media. It offers an essential tool for addressing the growing risk coming from more advanced deep fake solutions.