Written by : Khushi Jain, Abdul Waheed, Pappu Ram, Aman Mansoor
Vivekanand Global University, Jaipur
Abstract
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.
OBJECTIVES:-
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.
HYPOTHESES
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.
INTRODUCTION
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.
Conclusion:-
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.
Bibliography (Refrences)
1. TIMES OF INDIA by
2. PWONYLASIS
3. IPLEADERS
4. LAWBOTPRO
5. MANUPATRA
7. LAWSTUDIES
8. WIKIPEDIA
Comments