Digital image forgery detection is crucial in image forensics, aiming to identify manipulated regions and preserve visual integrity. Our framework combines Error Level Analysis (ELA) with prominent Convolutional Neural Network (CNN) architectures (VGG-16, VGG-19, ResNet-50, and Xception) to detect forgeries. ELA exploits error-level inconsistencies from manipulation, while CNN architectures extract features. We compare ELA with patch-level techniques, demonstrating its superior accuracy in capturing subtle artifacts. Experiments on the CASIA1 dataset evaluate the framework using metrics such as loss, accuracy, recall, precision, F1-score, and computational time. Results confirm the framework’s effectiveness in accurately detecting forgeries. Computational time analysis highlights its efficiency for real-world applications. In conclusion, our research presents a comprehensive framework using ELA and CNN architectures, showcasing ELA’s superiority and the potential of integrating it with CNNs for efficient forgery detection. This work advances image forensics, benefiting researchers and practitioners.