Images are crucial in various domains, including journalism, security, and healthcare, as they convey significant amounts of information. However, with the advancement of digital editing tools and the increasing prevalence of counterfeit images, the need for effective image forgery detection has become more critical than ever. Conventional image forgery detection techniques struggle to keep pace with evolving manipulation methods, often requiring extensive manual intervention and computational resources. This paper proposes Image Forgery Detection Network(IFDNet) architecture for image forgery detection task. The proposed architecture is an effective and efficient Deep Learning (DL) architecture incorporating newly introduced Dynamic Contextual Modulation Block (DCMB) for enhanced feature extraction and classification. The proposed IFDNet model is evaluated on widely used publicly available image forgery datasets like CASIA V2.0, and MICC-F2000 demonstrating its superior performance in differentiating authentic and manipulated images. Experimental results show that the proposed IFDNet model achieves high accuracy, precision, and recall, outperforming conventional approaches. The integration of DCMB significantly improves feature representation, enabling robust detection of complex forgeries. This advanced approach provides an efficient and scalable solution for image forgery detection, making a significant contribution to the field of digital image forensics.