Deepfake Detection and Mitigation Using Advanced CNN: Ensuring Digital Content Integrity
Palvi Sharma, Shubham Gupta, Mohit Sharma, Neha Gupta · 2025
Deepfake technology, with its hyper-realistic fake content and backed by AI technologies, presents a serious threat to the authenticity and security of digital content. The study deployed three different machine learning-based models: Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Vision Transformers (ViT) for discrimination of real and fake faces. The model leverages the capabilities of CNN for automatic spatial feature learning from raw data, giving it exceptional performance in the discrimination between real and fake content. Performance measures were made in terms of accuracy, precision, recall, and F1 score and compared against traditional methods; the model remains a trustworthy contribution towards deepfake detection. Findings indicate that the CNN exceeded others with the highest precisions of up to 92.21%, recall of 90%, F1 score of 93%, and perfect accuracy of 97.25%. The paper further discusses the future developments, for instance, architectural improvements and real-time applications and expanding detection capacity outside visual data for a more formidable approach towards new deepfake formats, such as audio and text. This endeavor lays the groundwork for viable and scalable roads against the rising onslaught on digital media manipulation, ensuring content integrity in an AI-driven world.