Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{186197,
        author = {Dr.Ganesh Gorakhnath Taware and Ms Sawkar Mayuri Bhausaheb and Ms Akanksha nagnath pansare},
        title = {Ai driven cyber threats and deepfake Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {616-620},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186197},
        abstract = {Artificial Intelligence (AI) has revolutionized the digital world by enabling automation, data- driven decision-making, and intelligent systems. However, this technological progress has also introduced new forms of cyber threats that are more sophisticated, adaptive, and harder to detect. AI-driven cyber threats leverage machine learning algorithms to automate attacks such as phishing, malware generation, identity theft, and network intrusion. Unlike traditional cyberattacks, these AI-powered threats continuously learn and evolve, making conventional security measures less effective. One of the most alarming consequences of this advancement is the rise of deepfakes hyper realistic synthetic media created using deep learning models, particularly Generative Adversarial Networks (GANs). Deepfakes can manipulate audio, video, and images to impersonate individuals, spread misinformation, and damage reputations, posing serious threats to privacy, politics, and social trust.
AI-driven deepfake technology is increasingly being exploited for malicious purposes, including fraud, social engineering, and cyber extortion. Attackers can now create convincing fake identities or manipulate official communications to deceive users or organizations. The rapid evolution of these technologies challenges existing cybersecurity frameworks, demanding advanced countermeasures powered by AI itself. Defenses such as deepfake detection algorithms, behavior-based threat analysis, and explainable AI (XAI) are being developed to combat these risks.},
        keywords = {Artificial Intelligence (AI), Cybersecurity, AI-driven threats, Deepfake, Machine Learning, Generative Adversarial Networks (GANs), Misinformation, Identity Theft, Phishing, Social Engineering, Data Privacy, Digital Security, Threat Detection, Ethical AI, Cybercrime.},
        month = {November},
        }