One notable instance where I applied deep learning to a problem traditionally solved with classical algorithms was in an image tampering detection project. Traditionally, this problem has been addressed using methods like Error Level Analysis (ELA), which detect inconsistencies in compression levels within an image to identify tampering. While effective in certain scenarios, ELA often struggles with subtle manipulations and tends to generate high false-positive rates. To overcome these limitations, we implemented a deep learning-based solution using a pre-trained convolutional neural network (CNN). Specifically, we fine-tuned a model trained on image forensics datasets to identify tampering patterns, such as splicing, copy-move forgery, and alterations to compression artifacts. The deep learning approach allowed the model to learn intricate features that were not easily captured by classical algorithms. The result was a significant improvement in performance, with detection accuracy increasing to approximately 95%, compared to around 70%-75% with classical methods. Additionally, the deep learning model demonstrated greater robustness to various types of manipulations, including subtle edits that would have gone undetected by traditional ELA techniques. This approach not only enhanced accuracy but also streamlined the tampering detection process by reducing the need for manual adjustments and thresholds often required with classical methods. It demonstrated the power of deep learning in replacing and augmenting traditional algorithms for complex, real-world problems, ultimately providing a more reliable and scalable solution for image forensics.