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  • Khushi Jain, Abdul Waheed, Pappu Ram, Aman Mansoor  


Written by : Khushi Jain, Abdul Waheed, Pappu Ram, Aman Mansoor  

Vivekanand Global University, Jaipur    


Deepfakes allow for the automatic generation and creation of (fake) video content, e.g.  through generative adversarial networks. Deepfake technology is a controversial technology  with many wide reaching issues impacting society, e.g. election biasing. Much research has  been devoted to developing detection methods to reduce the potential negative impact of  deepfakes. Application of neural networks and deep learning is one approach. In this paper,  we consider the deepfake detection technologies Xception and MobileNet as two approaches  for classification tasks to automatically detect deepfake videos. We utilise training and  evaluation datasets from FaceForensics++ comprising four datasets generated using four  different and popular deepfake technologies. The results show high accuracy over all  datasets with an accuracy varying between 91-98% depending on the deepfake technologies applied. We also developed a voting mechanism that can detect fake videos using the  aggregation of all four methods instead of only one.

Research Methodology  

Begin by reviewing existing literature on deepfake laws in India. Explore academic journals, legal  databases, and relevant government publications to understand the historical context and current  status. Examine the Indian Penal Code, The Information Technology act, 2000 and other relevant  legislation. Analyze amendments and court decisions that may have impacted deepfake laws.  Investigate specific legal cases related to deepfake in India. Understand the judgments, legal  arguments, and implications of these cases on the interpretation and enforcement of deepfake laws.  Compare India's deepfake laws with those of other countries.  

Statement of Problem  

The statement of the problem regarding deepfake laws in India could address issues such as the  need for comprehensive and uniform regulations, ensuring privacy of every individual, Deepfakes  allow for the automatic generation and creation of (fake) video content, e.g. through generative  adversarial networks. Deepfake technology is a controversial technology with many wide reaching  issues impacting society, e.g. election biasing. Much research has been devoted to developing  detection methods to reduce the potential negative impact of deepfakes. Application of neural  networks and deep learning is one approach. In this paper, we consider the deepfake detection  technologies Xception and MobileNet as two approaches for classification tasks to automatically  detect deepfake videos. We utilise training and evaluation datasets from FaceForensics++  comprising four datasets generated using four different and popular deepfake technologies. The  results show high accuracy over all datasets with an accuracy varying between 91-98% depending  on the deepfake technologies applied. We also developed a voting mechanism that can detect fake  videos using the aggregation of all four methods instead of only one. 

Research Aim and objectives 

AIM:- To analyze and understand the current state of deepfake law in India, exploring its  legal framework, historical context, and implications for individuals privacy rights.


1 Examine the legal provisions surrounding deepfake in India, including relevant statutes and  regulations. 

2. Investigate the historical evolution of abortion laws in the country and the factors influencing  their development. 

3. Assess the impact of deepfake laws on the life of every individual  

4. Explore societal attitudes and cultural influences that shape perceptions of deepfake in India. 

5. Evaluate the effectiveness of existing legal provisions in safeguarding individual’s privacy, their  rights. 

6. Examine any recent amendments or proposed changes to deepfake laws and their potential  implications. 

7. Provide recommendations for potential improvements or reforms in the deepfake law to better  align with every individual’s health and rights. 


Deepfake technology has raised concerns about its potential misuse, including the creation of  convincing fake videos or audios. Some hypotheses revolve around its impact on misinformation,  privacy breaches, identity theft, and its potential use in various industries like entertainment or  even politics. There's also speculation on how advancements in deepfake detection might evolve  to counter these issues.


In the ever-evolving landscape of technology, artificial intelligence has introduced us to  numerous groundbreaking applications. One such innovation is the creation of deepfakes, a  blend of “deep learning” and “fake,” which enables us to manipulate digital content,  particularly faces, in unprecedented ways. This article delves into the captivating world of  face swapping using deepfakes and ROOP (Reality Object Oriented Programming), exploring  both the artistic possibilities and the ethical concerns that arise from these technologies. Deepfakes are produced by manipulating existing videos and images to produce realistic looking but wholly fake content. The rise of advanced artificial intelligence-based tools and  software that require no technical expertise has made deepfake creation easier. With the  unprecedented exponential advancement, the world is currently witnessing in generative  artificial intelligence, the research community is in dire need of keeping informed on the  most recent developments in deepfake generation and detection technologies to not fall  behind in this critical arms race. 

Chapter 1:- Introduction to deepfake 

Introduction to deepfake Deepfake is a type of artificial intelligence-based technology that  uses machine learning algorithms, particularly generative adversarial networks (GANs), to  generate synthetic media such as images, videos, and audios. The goal of deepfake  technology is to create highly realistic synthetic media that resembles real people, but with  some aspect of the content manipulated.  

1.1:- Creation of Deepfakes 

Deepfakes are created using a machine learning technique known as generative adversarial  networks (GANs). A GAN consists of two neural networks, a generator, and a discriminator,  that are trained on a large dataset of real images, videos, or audio. The generator network  creates synthetic data, such as a synthetic image, that resembles the real data in the training  set. The discriminator network then assesses the authenticity of the synthetic data and  provides feedback to the generator on how to improve its output. This process is repeated  multiple times, with the generator and discriminator learning from each other, until the generator produces synthetic data that is highly realistic and difficult to distinguish from the  real data. This training data is used to create deepfakes which may be applied in various ways  for video and image deepfakes: (a) face swap: transfer the face of one person for that of the  person in the video; (b) attribute editing: change characteristics of the person in the video  e.g. style or colour of the hair; (c) face re-enactment: transferring the facial expressions from  the face of one person on to the person in the target video; and (d) fully synthetic material:  Real material is used to train what people look like,but the resulting picture is entirely made  up. 

1.2:- Detection of Deepfake 

It is important to note that deepfake technology is constantly evolving and improving, so  deepfake detection techniques need to be regularly updated to keep up with the latest  developments. Currently, the best way to determine if a piece of media is a deepfake is to use  a combination of multiple detection techniques and to be cautious of any content that seems  too good to be true. Here are some of the most common techniques used to detect deepfakes:  Visual artifacts. — Some deepfakes have noticeable visual artifacts3, such as unnatural facial  movements or blinking, that can be a giveaway that the content is fake. Visual artifacts in  deepfakes can arise due to several factors, such as limitations in the training data, limitations  in the deep learning algorithms, or the need to compromise between realism and  computational efficiency. Some common examples of visual artifacts in deepfakes include  unnatural facial movements or expressions, unnatural or inconsistent eye blinking, and  mismatched or missing details in the background. Audio-visual mismatches. — In some  deepfakes, the audio and visual content may not match perfectly, which can indicate that the  content has been manipulated. For example, the lip movements of a person in a deepfake  video may not match the audio perfectly, or the audio may contain background noise or  echoes that are not present in the video4. These types of audio-visual mismatches can be a  sign that the content has been manipulated. Deep learning-based detection. — Deep learning  algorithms, such as deep neural networks, can be used to detect deepfakes by training the algorithms on a large dataset of real and fake images, videos, or audios. The algorithm learns the patterns and artifacts that are typical of fake content, such as unnatural facial  movements, inconsistent eye blinking, and audio-visual mismatches.Once the deep learning  algorithm has been trained, it can be used to detect deepfakes by analysing new, unseen  media. If the algorithm detects that a piece of media is fake, it can flag it for manual inspection  or flag it for further analysis.

Chapter 2:- Deepfake and reality manipulation 

2.1:- Unveiling the Magic of Deepfakes and ROOP: 

Deepfakes, powered by neural networks and machine learning algorithms, can convincingly  replace one person’s face with another in videos or images. On the other hand, Reality Object  Oriented Programming (ROOP) provides a powerful framework for creating interactive and  immersive digital experiences that can alter reality in real-time. The fusion of these two  technologies opens the door to a new realm of creative expression, enabling artists and  developers to reimagine storytelling, entertainment, and even education. 

2.2:- The Artistic Marvels of Face Swapping: 

Imagine a world where actors can effortlessly slip into historical roles or portray characters  in ways previously deemed impossible. Deepfake and ROOP technologies offer artists the  tools to transform performances, turning ordinary scenes into extraordinary spectacles. A  filmmaker, for instance, can use these techniques to showcase an actor’s versatility by  seamlessly swapping faces, thus providing an entirely fresh perspective on storytelling. 

2.3:- Pushing the Boundaries of Reality: 

ROOP takes the concept of digital art to the next level by allowing users to actively participate  in, modify, and reshape virtual environments. For instance, a gaming experience could  become intensely immersive as players personalize their avatars with their own faces or  those of their favorite celebrities. This interconnectedness between digital and real-world  elements illustrates the potential of technology to redefine how we interact with our  surroundings.

2.4:- The Ethical Quandaries: 

However, this brave new world is not without its ethical concerns. The ease with which  deepfakes can be created has raised alarms about the potential misuse of this technology for  malicious purposes, including spreading misinformation, identity theft, or compromising  personal privacy. As artists and developers push the boundaries of creativity, society must  grapple with the implications of blurring the lines between reality and digital fabrication. 

2.5:- Navigating the Moral Compass: 

The responsible use of deepfakes and ROOP lies in the hands of creators, consumers, and  policymakers alike. Implementing safeguards to ensure proper attribution, consent, and  transparency becomes crucial to maintaining the integrity of digital content. Striking a  balance between artistic freedom and ethical considerations will define how these  technologies shape our future.

Chapter 3:- Offences Committed by using Deepfakes 

There is a plethora of possibility of commission of crimes using the technology of deepfake.  The technology itself does not pose a threat, however it can be used as a tool to commit  crimes against individuals and society. The following crimes can be committed using  deepfake: Identity theft and virtual forgery.  

3.1:- Identity theft and virtual forgery using deepfakes can be serious offences and can have  significant consequences for individuals and society as a whole. The use of deepfakes to steal  someone’s identity, create false representations of individuals, or manipulate public opinion  can cause harm to an individual’s reputation and credibility, and can spread misinformation.  Under Section 668 computer-related offences) and Section 66-C9 (punishment for identity  theft) of the Information Technology Act, 2000 these crimes can be prosecuted. Also, Sections  42010 and 46811 of the Penal Code, 1860 could be invoked in this regard. Misinformation  against Governments.  

3.2:- The use of deepfakes to spread misinformation, subvert the Government, or incite  hatred and disaffection against the Government is a serious issue and can have far-reaching  consequences for society. The spread of false or misleading information can create confusion  and undermine public trust and can be used to manipulate public opinion or influence  political outcomes. Under Section 66-F12 (cyber terrorism) and the Information Technology  (Intermediary Guidelines and Digital Media Ethics Code) Amendment Rules, 202213 of the  Information Technology Act, 200014 these crimes can be prosecuted. Also, Section 12115  waging war against the Government of India) and Section 124-A16 of the Penal Code, 1860  could be invoked in this regard. Hate speech and online defamation. 

3.3:- Hate speech and online defamation using deepfakes can be serious issues that can harm  individuals and society as a whole. The use of deepfakes to spread hate speech or defamatory  content can cause significant harm to the reputation and well-being of individuals and can  contribute to a toxic online environment. Under the Information Technology (Intermediary  Guidelines and Digital Media Ethics Code) Amendment Rules, 2022 of the Information  Technology Act, 2000 these crimes can be prosecuted. Also, Sections 153-A17 and 153-B18  

(Speech affecting public tranquility) Section 49919 (defamation) of the Penal Code, 1860  could be invoked in this regard. Practices affection elections. 

3.4:- The use of deepfakes in elections can have significant consequences and can undermine  the integrity of the democratic process. Deepfakes can be used to spread false or misleading  information about political candidates and can be used to manipulate public opinion and  influence the outcome of an election.The impact of deepfakes on elections is a growing  concern, and many Governments and organisations are taking steps to address this issue.  Under Section 66-D20 (punishment for cheating by personation by using computer  resource) and Section 66-F21 (cyber terrorism) of the Information Technology Act, 2000  these crimes can be prosecuted. Also, Sections 123(3-A)22, 123 and 12523 of the  Representation of the People Act, 1951 and Social Media Platforms and Internet and Mobile  Association of India (IAMAI), today presented a “Voluntary Code of Ethics for the General  Election, 2019 could be invoked to tackle the menace affecting elections in India. Violation of  privacy/obscenity and pornography. 

3.5:- This technology can be used to create fake images or videos that depict people doing or  saying things that never actually happened, potentially damaging the reputation of  individuals, or spreading false information. It is also possible for deepfakes to be used for  malicious purposes such as non-consensual pornography, or for political propaganda or  misinformation campaigns. This can have serious implications for individuals whose images  or likenesses are used without their consent, as well as for society at large when deepfakes  are used to spread false information or manipulate public opinion. Under Section 66-E24  (punishment for violation of privacy), Section 6725 (punishment for publishing or  transmitting obscene material in electronic form), Section 67-A26 (punishment for  publishing or transmitting of material containing sexually explicit act, etc. in electronic form),  Section 67-B27 (punishment for publishing or transmitting of material depicting children  sexually explicit act/pornography in electronic form) of the Information Technology Act,  2000 these crimes can be prosecuted. Also, Sections 29228 and 29429 (Punishment for sale  etc. of obscene material) of the Penal Code, 1860 and Sections 1330, 1431 and 1532 of the  Protection of Children from Sexual Offences Act, 2012 (POCSO) could be invoked in this  regard to protect the rights of women and children.


Chapter 4:- The Ethical Implications of Deepfake Technology 

Deepfake technology while remarkable from a standpoint raises ethical concerns: 

4.1:- Misrepresentation & Deception: The primary concern with deepfakes lies in their  ability to convincingly portray individuals saying or doing things they never actually did. This  can greatly infringe on an individual's right to their image and reputation. 

4.2:- Privacy Violation: The technology often utilizes images and videos without obtaining  consent, from the individuals involved. This unauthorized use of data raises privacy issues. 

4.3:- Spread of Misinformation: Deepfakes have the potential to be used for spreading  information generating news and fueling disinformation campaigns. The potential  consequences of this are quite concerning, in the realm of politics. Deepfakes have the ability  to sway opinion. Even influence election outcomes, which is alarming. 

4.4:- Cybersecurity Threats: One area where deepfakes pose a cybersecurity threat is in  phishing scams. These scams could involve using a fabricated video of someone to deceive  victims into sharing information. 

4.5:- Legal Challenges: Another challenge lies in the legal domain. Current laws may not  adequately address the issues brought about by deepfakes leaving accountability for those  who misuse this technology. 

To sum it up while deepfake technology can have applications like entertainment or historical  reenactments it is crucial to establish regulations and sophisticated detection methods.  Failing to do so could result in reaching ethical implications that can cause harm. It’s essential  for lawmakers, tech companies, and society, as a whole to fully grasp these implications and  take measures to manage them appropriately.

Chapter 5:- The Risks Associated with Deepfake Technology 

The risks associated with deepfake technology are multi-faceted and have serious  implications across various sectors. At the core, these dangers emerge from the realistic and  convincing artificial videos or images that deepfakes generate, often misleading viewers into  believing in the authenticity of such content. 

5.1:- Authenticity Breach: As artificial intelligence is employed to create deepfakes it poses  a challenge, to the traditional notion of authenticity. The belief in what we see is no longer  reliable as deepfakes have the ability to convincingly fabricate individuals engaging in actions  they never actually performed, leading to an erosion in trust when it comes to content. 

5.2:- Violations of Privacy: Deepfakes bring forth concerns regarding privacy. With a  handful of available images or videos, deepfake technology can recreate and manipulate  someone's appearance or voice opening the door for potential misuse and intrusion into their  private lives. 

5.3:- Political Disruption: The political landscape is particularly susceptible to the impacts  of deepfakes. These advancements can generate fabricated speeches or actions that have the  potential to sway opinion create unrest and even influence election results. 

5.4:- Legal Challenges: One of the obstacles in combating deepfakes lies in the absence of  legislation specifically addressing this technology. The lack of framework means that  individuals who maliciously create or utilize deepfakes often face legal consequences. 

Given these risks, it becomes crucial to develop defense mechanisms against deepfakes. This  could manifest through detection algorithms, strict regulations, or public awareness  campaigns highlighting the dangers associated with deepfake technology. Without measures  in place, the widespread availability and increasing sophistication of this technology could  pose threats to individual security as well as our society and nation, as a whole.

Chapter 6:- Combating Risks and Misuse 

To combat the risks and potential misuse of deepfake technology there are strategies that can  be implemented; 

6.1:- Raising Awareness: It is crucial to educate the public about the existence and potential  dangers of deepfakes. By providing people with knowledge, about deepfakes, we can cultivate  a discerning audience who are less likely to fall victim to deceptive content. 

6.2:- Detection Technology: Investing in algorithms for detection is a countermeasure. By  utilizing machine learning and AI we can develop technologies for identifying deepfakes by  analyzing cues such as lighting, shadows, or inconsistencies in facial movements that often go  unnoticed by humans. 

6.3:- Regulation: Implementing frameworks can help prevent the misuse of deepfake  technology. This could involve enacting laws that specifically criminalize the use or  mandating the disclosure of manipulated content created through deepfake technology. 

6.4:- Authentication: Another promising solution involves utilizing media authentication  techniques. Having a system in place that verifies and certifies the authenticity of content at  its creation establishes a chain of custody making it more difficult for deepfakes to go  undetected. 

6.5:- Collaboration: Fostering collaboration plays a role, in combating deepfakes effectively.  By working across countries and industries we can combine resources, exchange insights, and  coordinate responses to effectively address this challenge.

Chapter 7:- Different Acts and laws on deepfake in india 

India lacks specific laws to address deepfakes and AI-related crimes, but provisions under  a plethora of legislations could offer both civil and criminal relief. For instance, Section 66E  of the Information Technology Act, 2000 (IT Act) is applicable in cases of deepfake crimes  that involve the capture, publication, or transmission of a person’s images in mass media  thereby violating their privacy. Such an offence is punishable with up to three years of  imprisonment or a fine of ₹2 lakh. Similarly, Section 66D of the IT Act punishes individuals  who use communication devices or computer resources with malicious intent, leading to  impersonation or cheating. An offence under this provision carries a penalty of up to three  years imprisonment and/or a fine of ₹1 lakh. 

Further, Sections 67, 67A, and 67B of the IT Act can be used to prosecute individuals for  publishing or transmitting deepfakes that are obscene or contain any sexually explicit acts.  The IT Rules, also prohibit hosting ‘any content that impersonates another person’ and  require social media platforms to quickly take down ‘artificially morphed images’ of  individuals when alerted. In case they fail to take down such content, they risk losing the  ‘safe harbour’ protection — a provision that protects social media companies from  regulatory liability for third-party content shared by users on their platforms. 

Provisions of the Indian Penal Code, 1860, (IPC) can also be resorted to for cybercrimes  associated with deepfakes — Sections 509 (words, gestures, or acts intended to insult the  modesty of a woman), 499 (criminal defamation), and 153 (a) and (b) (spreading hate on  communal lines) among others. The Delhi Police Special Cell has reportedly registered an  FIR against unknown persons by invoking Sections 465 (forgery) and 469 (forgery to harm  the reputation of a party) in the Mandanna case. 

Apart from this, the Copyright Act of 1957 can be used if any copyrighted image or video  has been used to create deepfakes. Section 51 prohibits the unauthorised use of any  property belonging to another person and on which the latter enjoys an exclusive right.


In conclusion, it is important to acknowledge the risks associated with the misuse of deepfake  technology. However, by taking an approach that includes raising awareness implementing  detection technology enacting regulations establishing authentication measures, and  fostering collaboration we can effectively combat these threats and create a safer digital  environment. 

In closing the emergence of deepfake technology brings both possibilities and concerns. On  one hand, it has the potential to revolutionize fields, like entertainment and personalized  advertising. On the other hand, it also poses risks to personal privacy, security, and  democratic processes. 

Awareness: Increasing awareness about deepfakes within society is crucial in protecting  ourselves from these risks. By enhancing people's understanding of deepfakes existence and  implications we empower individuals to question and critically evaluate content. 

Technology: Furthermore leveraging advancements in machine learning and artificial  intelligence plays a role in this fight against deepfakes. Developing technologies that can  detect cues often overlooked by humans will enable us to differentiate between deepfakes  and authentic content. 

Regulations: Implementing regulations that govern the usage of deepfake technology is  another aspect of addressing this issue. These regulations should encompass aspects such as  creation guidelines, distribution restrictions, and penalties, for use. We should strongly advocate for regulations to address the misuse of deepfake technology. Enacting laws that  criminalize the use of deepfakes and mandate the disclosure of manipulated content can offer  legal remedies. 

Proof of Authenticity: Implementing media authentication techniques can help verify and  certify the genuineness of content, from its creation point establishing a traceable digital  chain of custody that makes it more difficult for deepfakes to go undetected. 

Collaborative Efforts: International cooperation plays a role in combating the threat posed  by deepfakes. By pooling resources sharing insights and coordinating responses we can  effectively tackle this challenge. 

In summary, countering the risks associated with deepfake technology necessitates a  multifaceted approach. It is, through raising awareness, technological advancements,  regulatory frameworks, authentication techniques, and international collaboration that we  can aspire to mitigate the dangers posed by deepfakes and promote an environment. The  current legislation in India regarding cyber offences caused using deepfakes is not adequate  to fully address the issue. The lack of specific provisions in the IT Act, 2000 regarding  artificial intelligence, machine learning, and deepfakes makes it difficult to effectively  regulate the use of these technologies. In order to better regulate offences caused using  deepfakes, it may be necessary to update the IT Act, 2000 to include provisions that  specifically address the use of deepfakes and the penalties for their misuse. This could  include increased penalties for those who create or distribute deepfakes for malicious  purposes, as well as stronger legal protections for individuals whose images or likenesses  are used without their consent. It is also important to note that the development and use of  deepfakes is a global issue, and it will likely require international cooperation and collaboration to effectively regulate their use and prevent privacy violations.In the  meantime, it is important for individuals and organisations to be aware of the potential risks  associated with deepfakes and to be vigilant in verifying the authenticity of information  encountered online. In the meantime, the Governments can do the following: (a) First, is the  censorship approach of blocking public access to misinformation by issuing orders to  intermediaries and publishers. (b) Second approach is the punitive approach which imposes  liability on individuals or organisations originating or disseminating misinformation. (c) The  third approach is the intermediary regulation approach, which imposes obligations upon  online intermediaries to expeditiously remove misinformation from their platforms, failing  which they could incur liability as stipulated under Sections 69-A33 and 7934 of Information  Technology Act, 2000.


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