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In the vast, uncharted territories of the digital world, a sinister phenomenon is proliferating at an alarming rate. It's a world where artificial intelligence (AI) and human vulnerability intertwine in a disturbing combination, creating a shadowy realm of non-consensual pornography. This is the world of deepfake pornography, a burgeoning industry that is as lucrative as it is unsettling.

According to a recent assessment, at least 100,000 deepfake porn videos are readily available on the internet, with hundreds, if not thousands, being uploaded daily. This staggering statistic prompts a chilling question: what is driving the creation of such a vast number of fakes? Is it merely for amusement, or is there a more sinister motive at play?

Recent Trends and Developments 

An investigation by India Today’s Open-Source Intelligence (OSINT) team reveals that deepfake pornography is rapidly morphing into a thriving business. AI enthusiasts, creators, and experts are extending their expertise, investors are injecting money, and even small financial companies to tech giants like Google, VISA, Mastercard, and PayPal are being misused in this dark trade. Synthetic porn has existed for years, but advances in AI and the increasing availability of technology have made it easier—and more profitable—to create and distribute non-consensual sexually explicit material. The 2023 State of Deepfake report by Home Security Heroes reveals a staggering 550% increase in the number of deepfakes compared to 2019. 

What’s the Matter with Fakes? 

But why should we be concerned about these fakes? The answer lies in the real-world harm they cause. India has already seen cases of extortion carried out by exploiting deepfake technology. An elderly man in UP’s Ghaziabad, for instance, was tricked into paying Rs 74,000 after receiving a deep fake video of a police officer. The situation could have been even more serious if the perpetrators had decided to create deepfake porn of the victim.

The danger is particularly severe for women. The 2023 State of Deepfake Report estimates that at least 98 percent of all deepfakes is porn and 99 percent of its victims are women. A study by Harvard University refrained from using the term “pornography” for creating, sharing, or threatening to create/share sexually explicit images and videos of a person without their consent. “It is abuse and should be understood as such,” it states.

Based on interviews of victims of deepfake porn last year, the study said 63 percent of participants talked about experiences of “sexual deepfake abuse” and reported that their sexual deepfakes had been monetised online. It also found “sexual deepfake abuse to be particularly harmful because of the fluidity and co-occurrence of online offline experiences of abuse, resulting in endless reverberations of abuse in which every aspect of the victim’s life is permanently disrupted”.

Creating deepfake porn is disturbingly easy. There are largely two types of deepfakes: one featuring faces of humans and another featuring computer-generated hyper-realistic faces of non-existing people. The first category is particularly concerning and is created by superimposing faces of real people on existing pornographic images and videos—a task made simple and easy by AI tools.

During the investigation, platforms hosting deepfake porn of stars like Jennifer Lawrence, Emma Stone, Jennifer Aniston, Aishwarya Rai, Rashmika Mandanna to TV actors and influencers like Aanchal Khurana, Ahsaas Channa, and Sonam Bajwa and Anveshi Jain were encountered. It takes a few minutes and as little as Rs 40 for a user to create a high-quality fake porn video of 15 seconds on platforms like FakeApp and FaceSwap.

The Modus Operandi 

These platforms brazenly flaunt their business association and hide behind frivolous declarations such as: the content is “meant solely for entertainment” and “not intended to harm or humiliate anyone”. However, the irony of these disclaimers is not lost on anyone, especially when they host thousands of non-consensual deepfake pornography.

As fake porn content and its consumers surge, deepfake porn sites are rushing to forge collaborations with generative AI service providers and have integrated their interfaces for enhanced interoperability. The promise and potential of making quick bucks have given birth to step-by-step guides, video tutorials, and websites that offer tools and programs, recommendations, and ratings.

Nearly 90 per cent of all deepfake porn is hosted by dedicated platforms that charge for long-duration premium fake content and for creating porn—of whoever a user wants, and take requests for celebrities. To encourage them further, they enable creators to monetize their content.

One such website, Civitai, has a system in place that pays “rewards” to creators of AI models that generate “images of real people'', including ordinary people. It also enables users to post AI images, prompts, model data, and LoRA (low-rank adaptation of large language models) files used in generating the images. Model data designed for adult content is gaining great popularity on the platform, and they are not only targeting celebrities. Common people are equally susceptible.

Access to premium fake porn, like any other content, requires payment. But how can a gateway process payment for sexual content that lacks consent? It seems financial institutes and banks are not paying much attention to this legal question. During the investigation, many such websites accepting payments through services like VISA, Mastercard, and Stripe were found.

Those who have failed to register/partner with these fintech giants have found a way out. While some direct users to third-party sites, others use personal PayPal accounts to manually collect money in the personal accounts of their employees/stakeholders, which potentially violates the platform's terms of use that ban the sale of “sexually oriented digital goods or content delivered through a digital medium.”

 Among others, the MakeNude.ai web app – which lets users “view any girl without clothing” in “just a single click” – has an interesting method of circumventing restrictions around the sale of non-consensual pornography. The platform has partnered with Ukraine-based Monobank and Dublin’s BetaTransfer Kassa which operates in “high-risk markets”.

BetaTransfer Kassa admits to serving “clients who have already contacted payment aggregators and received a refusal to accept payments, or aggregators stopped payments altogether after the resource was approved or completely freeze your funds”. To make payment processing easy, MakeNude.ai seems to be exploiting the donation ‘jar’ facility of Monobank, which is often used by people to donate money to Ukraine to support it in the war against Russia.

The Indian Scenario 

India currently is on its way to design dedicated legislation to address issues arising out of deepfakes. Though existing general laws requiring such platforms to remove offensive content also apply to deepfake porn. However, persecution of the offender and their conviction is extremely difficult for law enforcement agencies as it is a boundaryless crime and sometimes involves several countries in the process.

A victim can register a police complaint under provisions of Section 66E and Section 66D of the IT Act, 2000. Recently enacted Digital Personal Data Protection Act, 2023 aims to protect the digital personal data of users. Recently Union Government issued an advisory to social media intermediaries to identify misinformation and deepfakes. Comprehensive law promised by Union IT minister Ashwini Vaishnav will be able to address these challenges.

Conclusion 

In the end, the unsettling dance of AI and human vulnerability continues in the dark web of deepfake pornography. It's a dance that is as disturbing as it is fascinating, a dance that raises questions about the ethical use of technology, the protection of individual rights, and the responsibility of financial institutions. It's a dance that we must all be aware of, for it is a dance that affects us all.

References

• https://www.indiatoday.in/india/story/deepfake-porn-artificial-intelligence-women-fake-photos-2471855-2023-12-04
• https://www.hindustantimes.com/opinion/the-legal-net-to-trap-peddlers-of-deepfakes-101701520933515.html
• https://indianexpress.com/article/opinion/columns/with-deepfakes-getting-better-and-more-alarming-seeing-is-no-longer-believing/

Introduction

India's Competition Commission of India (CCI) on 18th November 2024 imposed a ₹213 crore penalty on Meta for abusing its dominant position in internet-based messaging through WhatsApp and online display advertising. The CCI order is passed against abuse of dominance by the Meta and relates to WhatsApp’s 2021 Privacy Policy. The CCI considers Meta a dominant player in internet-based messaging through WhatsApp and also in online display advertising. WhatsApp's 2021 privacy policy update undermined users' ability to opt out of getting their data shared with the group's social media platform Facebook. The CCI directed WhatsApp not to share user data collected on its platform with other Meta companies or products for advertising purposes for five years.

CCI Contentions

The regulator contended that for purposes other than advertising, WhatsApp's policy should include a detailed explanation of the user data shared with other Meta group companies or products specifying the purpose. The regulator also stated that sharing user data collected on WhatsApp with other Meta companies or products for purposes other than providing WhatsApp services should not be a condition for users to access WhatsApp services in India. CCI order is significant as it upholds user consent as a key principle in the functioning of social media giants, similar to the measures taken by some other markets.

Meta’s Stance  

WhatsApp parent company Meta has expressed its disagreement with the Competition Commission of India's(CCI) decision to impose a Rs 213 crore penalty on them over users' privacy concerns. Meta clarified that the 2021 update did not change the privacy of people's personal messages and was offered as a choice for users at the time. It also ensured no one would have their accounts deleted or lose functionality of the WhatsApp service because of this update.

Meta clarified that the update was about introducing optional business features on WhatsApp and providing further transparency about how they collect data. The company stated that WhatsApp has been incredibly valuable to people and businesses, enabling organization's and government institutions to deliver citizen services through COVID and beyond and supporting small businesses, all of which further the Indian economy. Meta plans to find a path forward that allows them to continue providing the experiences that "people and businesses have come to expect" from them. The CCI issued cease-and-desist directions and directed Meta and WhatsApp to implement certain behavioral remedies within a defined timeline.

The competition watchdog noted that WhatsApp's 2021 policy update made it mandatory for users to accept the new terms, including data sharing with Meta, and removed the earlier option to opt-out, categorized as an "unfair condition" under the Competition Act.  It was further noted that WhatsApp’s sharing of users’ business transaction information with Meta gave the group entities an unfair advantage over competing platforms.

CyberPeace Outlook

The 2021 policy update by WhatsApp mandated data sharing with Meta's other companies group, removing the opt-out option and compelling users to accept the terms to continue using the platform. This policy undermined user autonomy and was deemed as an abuse of Meta's dominant market position, violating Section 4(2)(a)(i) of the Competition Act, as noted by CCI.

The CCI’s ruling requires WhatsApp to offer all users in India, including those who had accepted the 2021 update, the ability to manage their data-sharing preferences through a clear and prominent opt-out option within the app. This decision underscores the importance of user choice, informed consent, and transparency in digital data policies.

By addressing the coercive nature of the policy, the CCI ruling establishes a significant legal precedent for safeguarding user privacy and promoting fair competition. It highlights the growing acknowledgement of privacy as a fundamental right and reinforces the accountability of tech giants to respect user autonomy and market fairness. The directive mandates that data sharing within the Meta ecosystem must be based on user consent, with the option to decline such sharing without losing access to essential services.

References

●       https://www.thehindu.com/business/Industry/cci-fines-whatsapp-meta-2021-privacy-policy/article68883219.ece/amp/

●       https://www.livemint.com/companies/cci-imposes-penalty-meta-breach-competition-law-whatsapp-privacy-policy-update-advertising-11731947005402.html

●       https://www.outlookbusiness.com/corporate/whatsapp-parent-meta-disagrees-with-cci-says-its-policy-furthers-indian-economy

●       https://www.hindustantimes.com/world-news/disagree-meta-to-challenge-ccis-order-on-whatsapp-data-sharing-policy-101732013196875.html

●       https://lawbeat.in/news-updates/cci-imposes-rs21314-crore-penalty-meta-abuse-dominant-position-whatsapps-2021-privacy

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Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/