International Journal of Scientific Research and Engineering Development

The rapid advancements in artificial intelligence, machine learning, and deep learning have led to the emergence of powerful tools capable of manipulating visual media. Among these, deepfake technologies have gained prominence for their ability to generate hyper-realistic fake images that are nearly indistinguishable from authentic ones. While such innovations have found legitimate applications in fields like entertainment, art, and education, they have also been misused for malicious purposes— including misinformation campaigns, identity fraud, and reputational damage. Deepfake image generation algorithms can fabricate highly convincing counterfeit visuals by altering facial features, expressions, and contexts, often deceiving even the most discerning human observers. This poses a significant threat to digital trust and visual authenticity. As the sophistication of these techniques continues to evolve, the challenge of detecting manipulated images becomes increasingly complex. This paper presents a comprehensive survey of the tools, techniques, and algorithms used specifically for deepfake image detection. It explores the current landscape of detection methodologies, including both traditional forensic approaches and modern deep learning-based models. The study also highlights the key challenges in this domain—such as generalization across datasets, adversarial robustness, and realtime detection constraints. By tracing the evolution of deepfake image manipulation and critically evaluating the state-of-the-art detection strategies, this work aims to contribute to the development of more resilient and adaptive solutions for safeguarding visual integrity in the digital age.