Ministry of I&B Takes Action Against Obscene Content; Shuts Down 18 OTT Platforms

Introduction

As we delve deeper into the intricate, almost esoteric digital landscape of the 21st century, we are confronted by a new and troubling phenomenon that threatens the very bastions of our personal security. This is not a mere subplot in some dystopian novel but a harsh and palatable reality firmly rooted in today's technologically driven society. We must grapple with the consequences of the alarming evolution of cyber threats, particularly the sophisticated use of artificial intelligence in creating face swaps—a technique now cleverly harnessed by nefarious actors to undermine the bedrock of biometric security systems.

What is GoldPickaxe?

It was amidst the hum of countless servers and data centers that the term 'GoldPickaxe' began to echo, sending shivers down the spines of cybersecurity experts. Originating from the intricate web spun by a group of Chinese hackers as reported in Dark Reading. GoldPickaxe represents the latest in a long lineage of digital predators. It is an astute embodiment of the disguise, blending into the digital environment as a seemingly harmless government service app. But behind its innocuous facade, it bears the intent to ensnare and deceive, with the elderly demographic being especially susceptible to its trap.  

Victims, unassuming and trustful, are cajoled into revealing their most sensitive information: phone numbers, private details, and, most alarmingly, their facial data. These virtual reflections, intended to be the safeguard of one's digital persona, are snatched away and misused in a perilous transformation. The attackers harness such biometric data, feeding it into the arcane furnaces of deepfake technology, wherein AI face-swapping crafts eerily accurate and deceptive facsimiles. These digital doppelgängers become the master keys, effortlessly bypassing the sentinel eyes of facial recognition systems that lock the vaults of Southeast Asia's financial institutions.

Through the diligent and unyielding work of the research team at Group-IB, the trajectory of one victim's harrowing ordeal—a Vietnamese individual pilfered of a life-altering $40,000—sheds light on the severity of this technological betrayal. The advancements in deep face technology, once seen as a marvel of AI, now present a clear and present danger, outpacing the mechanisms meant to deter unauthorized access, and leaving the unenlightened multitude unaware and exposed.

Adding weight to the discussion, experts, a potentate in biometric technology, commented with a somber tone: 'This is why we see face swaps as a tool of choice for hackers. It gives the threat actor this incredible level of power and control.' This chilling testament to the potency of digital fraudulence further emphasizes that even seemingly impregnable ecosystems, such as that of Apple’s, are not beyond the reach of these relentless invaders.

New Threat

Emerging from this landscape is the doppelgänger of GoldPickaxe specifically tailored for the iOS landscape—GoldDigger's mutation into GoldPickaxe for Apple's hallowed platform is nothing short of a wake-up call. It engenders not just a single threat but an evolving suite of menaces, including its uncanny offspring, 'GoldDiggerPlus,' which is wielding the terrifying power to piggyback on real-time communications of the affected devices. Continuously refined and updated, these threats become chimeras, each iteration more elusive, more formidable than its predecessor.

One ingenious and insidious tactic exploited by these cyber adversaries is the diversionary use of Apple's TestFlight, a trusted beta testing platform, as a trojan horse for their malware. Upon clampdown by Apple, the hackers, exhibiting an unsettling level of adaptability, inveigle users to endorse MDM profiles, hitherto reserved for corporate device management, thereby chaining these unknowing participants to their will.

How To Protect

Against this stark backdrop, the question of how one might armor oneself against such predation looms large. It is a question with no simple answer, demanding vigilance and proactive measures. 

General Vigilance : Aware of the Trojan's advance, Apple is striving to devise countermeasures, yet individuals can take concrete steps to safeguard their digital lives.

Consider Lockdown Mode:  It is imperative to exhibit discernment with TestFlight installations, to warily examine MDM profiles, and seriously consider embracing the protective embrace of Lockdown Mode. Activating Lockdown Mode on an iPhone is akin to drawing the portcullis and manning the battlements of one's digital stronghold. The process is straightforward: a journey to the settings menu, a descent into privacy and security, and finally, the sanctification of Lockdown Mode, followed by a device restart. It is a curtailment of convenience, yes, but a potent defense against the malevolence lurking in the unseen digital thicket.

As 'GoldPickaxe' insidiously carves its path into the iOS realm—a rare and unsettling occurrence—it flags the possible twilight of the iPhone's vaunted reputation for tight security. Should these shadow operators set their sights beyond Southeast Asia, angling their digital scalpels towards the U.S., Canada, and other English-speaking enclaves, the consequences could be dire.

Conclusion

Thus, it is imperative that as digital citizens, we fortify ourselves with best practices in cybersecurity. Our journey through cyberspace must be cautious, our digital trails deliberate and sparse. Let the specter of iPhone malware serve as a compelling reason to arm ourselves with knowledge and prudence, the twin guardians that will let us navigate the murky waters of the internet with assurance, outwitting those who weave webs of deceit. In heeding these words, we preserve not only our financial assets but the sanctity of our digital identities against the underhanded schemes of those who would see them usurped.

References 

• https://www.timesnownews.com/technology-science/new-ios-malware-stealing-face-id-data-bank-infos-on-iphones-how-to-protect-yourself-article-107761568
• https://www.darkreading.com/application-security/ios-malware-steals-faces-defeat-biometrics-ai-swaps
• https://www.tomsguide.com/computing/malware-adware/first-ever-ios-trojan-discovered-and-its-stealing-face-id-data-to-break-into-bank-accounts

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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/

Introduction

Misinformation is a major issue in the AI age, exacerbated by the broad adoption of AI technologies. The misuse of deepfakes, bots, and content-generating algorithms have made it simpler for bad actors to propagate misinformation on a large scale. These technologies are capable of creating manipulative audio/video content, propagate political propaganda, defame individuals, or incite societal unrest. AI-powered bots may flood internet platforms with false information, swaying public opinion in subtle ways. The spread of misinformation endangers democracy, public health, and social order. It has the potential to affect voter sentiments, erode faith in the election process, and even spark violence. Addressing misinformation includes expanding digital literacy, strengthening platform detection capabilities, incorporating regulatory checks, and removing incorrect information.

AI's Role in Misinformation Creation

AI's growth in its capabilities to generate content have grown exponentially in recent years. Legitimate uses or purposes of AI many-a-times take a backseat and result in the exploitation of content that already exists on the internet. One of the main examples of misinformation flooding the internet is when AI-powered bots flood social media platforms with fake news at a scale and speed that makes it impossible for humans to track and figure out whether the same is true or false.

The netizens in India are greatly influenced by viral content on social media. AI-generated misinformation can have particularly negative consequences. Being literate in the traditional sense of the word does not automatically guarantee one the ability to parse through the nuances of social media content authenticity and impact. Literacy, be it social media literacy or internet literacy, is under attack and one of the main contributors to this is the rampant rise of AI-generated misinformation. Some of the most common examples of misinformation that can be found are related to elections, public health, and communal issues. These issues have one common factor that connects them, which is that they evoke strong emotions in people and as such can go viral very quickly and influence social behaviour, to the extent that they may lead to social unrest, political instability and even violence. Such developments lead to public mistrust in the authorities and institutions, which is dangerous in any economy, but even more so in a country like India which is home to a very large population comprising a diverse range of identity groups. 

Misinformation and Gen AI

Generative AI (GAI) is a powerful tool that allows individuals to create massive amounts of realistic-seeming content, including imitating real people's voices and creating photos and videos that are indistinguishable from reality. Advanced deepfake technology blurs the line between authentic and fake. However, when used smartly, GAI is also capable of providing a greater number of content consumers with trustworthy information, counteracting misinformation. 

Generative AI (GAI) is a technology that has entered the realm of autonomous content production and language creation, which is linked to the issue of misinformation. It is often difficult to determine if content originates from humans or machines and if we can trust what we read, see, or hear. This has led to media users becoming more confused about their relationship with media platforms and content and highlighted the need for a change in traditional journalistic principles.

We have seen a number of different examples of GAI in action in recent times, from fully AI-generated fake news websites to fake Joe Biden robocalls telling the Democrats in the U.S. not to vote. The consequences of such content and the impact it could have on life as we know it are almost too vast to even comprehend at present. If our ability to identify reality is quickly fading, how will we make critical decisions or navigate the digital landscape safely? As such, the safe and ethical use and applications of this technology needs to be a top global priority. 

Challenges for Policymakers

AI's ability to generate anonymous content makes it difficult to hold perpetrators accountable due to the massive amount of data generated. The decentralised nature of the internet further complicates regulation efforts, as misinformation can spread across multiple platforms and jurisdictions. Balancing the need to protect the freedom of speech and expression with the need to combat misinformation is a challenge. Over-regulation could stifle legitimate discourse, while under-regulation could allow misinformation to propagate unchecked. India's multilingual population adds more layers to already-complex issue, as AI-generated misinformation is tailored to different languages and cultural contexts, making it harder to detect and counter. Therefore, developing strategies catering to the multilingual population is necessary.

Potential Solutions

To effectively combat AI-generated misinformation in India, an approach that is multi-faceted and multi-dimensional is essential. Some potential solutions are as follows:

• Developing a framework that is specific in its application to address AI-generated content. It should include stricter penalties for the originator and spreader and dissemination of fake content in proportionality to its consequences. The framework should establish clear and concise guidelines for social media platforms to ensure that proactive measures are taken to detect and remove AI-generated misinformation.
• Investing in tools that are driven by AI for customised detection and flagging of misinformation in real time. This can help in identifying deepfakes, manipulated images, and other forms of AI-generated content. 

• The primary aim should be to encourage different collaborations between tech companies, cyber security orgnisations, academic institutions and government agencies to develop solutions for combating misinformation. 
• Digital literacy programs will empower individuals by training them to evaluate online content. Educational programs in schools and communities teach critical thinking and media literacy skills, enabling individuals to better discern between real and fake content.

Conclusion

AI-generated misinformation presents a significant threat to India, and it is safe to say that the risks posed are at scale with the rapid rate at which the nation is developing technologically. As the country moves towards greater digital literacy and unprecedented mobile technology adoption, one must be cognizant of the fact that even a single piece of misinformation can quickly and deeply reach and influence a large portion of the population. Indian policymakers need to rise to the challenge of AI-generated misinformation and counteract it by developing comprehensive strategies that not only focus on regulation and technological innovation but also encourage public education. AI technologies are misused by bad actors to create hyper-realistic fake content including deepfakes and fabricated news stories, which can be extremely hard to distinguish from the truth. The battle against misinformation is complex and ongoing, but by developing and deploying the right policies, tools, digital defense frameworks and other mechanisms, we can navigate these challenges and safeguard the online information landscape.

References:

• https://economictimes.indiatimes.com/news/how-to/how-ai-powered-tools-deepfakes-pose-a-misinformation-challenge-for-internet-users/articleshow/98770592.cms?from=mdr 
• https://www.dw.com/en/india-ai-driven-political-messaging-raises-ethical-dilemma/a-69172400 
• https://pure.rug.nl/ws/portalfiles/portal/975865684/proceedings.pdf#page=62 

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