<p>The widespread adoption of advanced image morphing techniques, including deepfakes, has posed significant challenges to cybersecurity, digital forensics, and biometric authentication. Morphed images are increasingly exploited for identity theft, misinformation campaigns, cyber extortion, and other fraudulent activities. The rapid advancement of generative AI technologies, such as large language models (LLMs) like ChatGPT and image generation platforms like MidJourney, has further exacerbated these threats. While these technologies have transformed creative and professional domains, they are also misused to produce highly convincing fake content, including deepfakes and morphed images.</p><p>In response to these emerging challenges, this paper proposes a Comprehensive System for Detecting and Verifying Counterfeit Images using Deep Neural Networks. The system integrates metadata analysis, AI-driven anomaly detection, and advanced deep learning models such as EfficientNet, DenseNet, ResNet, and VGG to improve detection accuracy. Extensive testing on diverse real-world datasets containing both authentic and morphed images demonstrates the system's ability to effectively identify morphing-related inconsistencies. By leveraging cutting-edge neural network architectures, this solution aims to strengthen digital trust and play a pivotal role in combating cybercrimes involving counterfeit images.</p>