Comprehensive Detection of Image Manipulations using Deep Learning

Digital forensics, security, and journalism have all faced difficulties as a result of the proliferation of deepfake technology and sophisticated image manipulation techniques like splicing, tampering, and faceswapping, which have seriously damaged the legitimacy and dependability of digital media. In order to overcome this, we present a novel multi-model deep learning framework that combines Error Level Analysis (ELA), Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Vision Transformers (ViT) to detect and classify image forgeries with remarkable robustness and precision. ViT outperforms conventional GAN- and CNN-based models without overfitting by using global attention mechanisms to assess image-wide associations and detect tiny anomalies in texturing, lighting, and structural details, resulting in over 95% accuracy in detecting deepfakes. CNNs detect pixellevel anomalies and structural inconsistencies, ELA highlights compression artifacts to identify tampered regions, and GANs improve training by producing a variety of synthetic forgeries and exposing the model to a range of manipulation techniques. In order to guarantee resilience under a variety of manipulation scenarios, the system was trained on an extensive dataset of actual and altered photos, utilizing preprocessing approaches such as data augmentation and class-weighted loss functions. This scalable solution is an essential tool for media verification, misinformation detection, and digital forensics, setting a new standard for protecting the integrity of digital content. Experimental results show state-of-the-art performance, with high precision and recall across all forgery types, even under difficult conditions like compression and post-processing.