Exploring Legal and Technical Challenges of Deepfake in India

Abstract

Deepfake technology, driven by advancements in artificial intelligence and machine learning, has rapidly transformed the digital landscape, presenting both innovative opportunities and significant threats. In India, the proliferation of deepfakes has introduced complex technical and legal challenges that require urgent examination. This paper delves into these challenges by analyzing the limitations of current deep-fake detection algorithms, which often fall short in accurately identifying advanced synthetic media. While detection models based on machine learning show promise, they are increasingly challenged by sophisticated manipulation techniques that produce realistic forgeries. Concurrently, India’s legal framework struggles to keep pace with deepfake-related threats, as existing laws such as the Information Technology Act 2000 and provisions within the Indian Penal Code provide insufficient protections, especially regarding privacy, consent, and cybersecurity. This study explores the balance between safeguarding freedom of expression and implementing legal protections against deepfake misuse. We propose a set of solutions, including targeted legal reforms, enhanced detection technologies, public awareness programs, and international cooperation, aimed at addressing these dual challenges. These strategies will help India effectively regulate deepfake technology while ensuring digital safety and societal trust.

Introduction

Deepfake technology, driven by AI and machine learning, enables hyper-realistic manipulation of images, videos, and audio, with uses ranging from entertainment to misinformation and fraud. India, with its large digital population, faces significant risks from deepfake misuse, including disinformation and privacy violations. Despite progress in technical detection methods, they struggle to keep up with improving deepfake quality. Legally, India’s current laws (Information Technology Act 2000, Indian Penal Code) inadequately address deepfake-specific issues, creating a regulatory gap.

The paper analyzes both technical detection methods and legal frameworks, proposing a balanced approach involving improved detection techniques, targeted legal reforms, and increased public awareness. It also discusses hardware capabilities (CPUs, GPUs, TPUs, FPGA/ASIC) for generating and detecting deepfakes, emphasizing GPUs as the best compromise for real-time edge AI applications. Edge AI offers privacy and efficiency advantages but faces challenges like limited hardware and battery life, which can be mitigated through model optimization techniques.

Future directions include optimizing models for edge devices, enhancing real-time detection, ensuring cross-platform compatibility, privacy-preserving AI, legal updates, expanded datasets, and real-world system testing to create a safer digital environment in India.

Conclusion

The rapid advancement of deepfake technology presents significant challenges and opportunities in various sectors, particularly in media, security, and privacy. This paper explored the technical and legal aspects of deepfake detection, highlighting the effectiveness of edge AI solutions for real-time detection. Hardware capability ies, such as GPUs and TPUs, play a crucial role in balancing performance, cost, and energy consumption for edge devices. However, optimization and efficiency remain critical for broader adoption in resource-constrained environments. On the legal front, there are substantial gaps in existing frameworks to address deepfake-related privacy violations, intellectual property concerns, defamation, and fraud. Legal systems must evolve to keep pace with these technological advancements to protect individuals and organizations from the harmful effects of deepfakes. Future work should focus on optimizing detection models, improving privacy protections, and developing cross-platform solutions. Collaboration between the tech and legal sectors will be essential to create a balanced approach that ensures deepfake detection systems are effective, scalable, and ethically deployed. In conclusion, while deepfakes pose significant risks, innovative solutions in edge AI and legal regulation can mitigate these threats, enabling safer, more secure digital environments.

References

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Copyright

Copyright © 2025 Zeeshan Shaikh, Eashaan Ambasana, Moiz Morbiwala, Prof. Vidya Sagvekar . This 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.