#FactCheck - Debunking Viral Photo: Tears of Photographer Not Linked to Ram Mandir Opening

Introduction

The advent of AI-driven deepfake technology has facilitated the creation of explicit counterfeit videos for sextortion purposes. There has been an alarming increase in the use of Artificial Intelligence to create fake explicit images or videos for sextortion.

What is AI Sextortion and Deepfake Technology

AI sextortion refers to the use of artificial intelligence (AI) technology, particularly deepfake algorithms, to create counterfeit explicit videos or images for the purpose of harassing, extorting, or blackmailing individuals. Deepfake technology utilises AI algorithms to manipulate or replace faces and bodies in videos, making them appear realistic and often indistinguishable from genuine footage. This enables malicious actors to create explicit content that falsely portrays individuals engaging in sexual activities, even if they never participated in such actions.

Background on the Alarming Increase in AI Sextortion Cases

Recently there has been a significant increase in AI sextortion cases. Advancements in AI and deepfake technology have made it easier for perpetrators to create highly convincing fake explicit videos or images. The algorithms behind these technologies have become more sophisticated, allowing for more seamless and realistic manipulations. And the accessibility of AI tools and resources has increased, with open-source software and cloud-based services readily available to anyone. This accessibility has lowered the barrier to entry, enabling individuals with malicious intent to exploit these technologies for sextortion purposes.

The proliferation of sharing content on social media

The proliferation of social media platforms and the widespread sharing of personal content online have provided perpetrators with a vast pool of potential victims’ images and videos. By utilising these readily available resources, perpetrators can create deepfake explicit content that closely resembles the victims, increasing the likelihood of success in their extortion schemes.

Furthermore, the anonymity and wide reach of the internet and social media platforms allow perpetrators to distribute manipulated content quickly and easily. They can target individuals specifically or upload the content to public forums and pornographic websites, amplifying the impact and humiliation experienced by victims.

What are law agencies doing?

The alarming increase in AI sextortion cases has prompted concern among law enforcement agencies, advocacy groups, and technology companies. This is high time to make strong Efforts to raise awareness about the risks of AI sextortion, develop detection and prevention tools, and strengthen legal frameworks to address these emerging threats to individuals’ privacy, safety, and well-being.

There is a need for Technological Solutions, which develops and deploys advanced AI-based detection tools to identify and flag AI-generated deepfake content on platforms and services. And collaboration with technology companies to integrate such solutions.

Collaboration with Social Media Platforms is also needed. Social media platforms and technology companies can reframe and enforce community guidelines and policies against disseminating AI-generated explicit content. And can ensure foster cooperation in developing robust content moderation systems and reporting mechanisms.

There is a need to strengthen the legal frameworks to address AI sextortion, including laws that specifically criminalise the creation, distribution, and possession of AI-generated explicit content. Ensure adequate penalties for offenders and provisions for cross-border cooperation.

Proactive measures to combat AI-driven sextortion

Prevention and Awareness: Proactive measures raise awareness about AI sextortion, helping individuals recognise risks and take precautions.

Early Detection and Reporting: Proactive measures employ advanced detection tools to identify AI-generated deepfake content early, enabling prompt intervention and support for victims.

Legal Frameworks and Regulations: Proactive measures strengthen legal frameworks to criminalise AI sextortion, facilitate cross-border cooperation, and impose offender penalties.

Technological Solutions: Proactive measures focus on developing tools and algorithms to detect and remove AI-generated explicit content, making it harder for perpetrators to carry out their schemes.

International Cooperation: Proactive measures foster collaboration among law enforcement agencies, governments, and technology companies to combat AI sextortion globally.

Support for Victims: Proactive measures provide comprehensive support services, including counselling and legal assistance, to help victims recover from emotional and psychological trauma.

Implementing these proactive measures will help create a safer digital environment for all.

Misuse of Technology

Misusing technology, particularly AI-driven deepfake technology, in the context of sextortion raises serious concerns.

Exploitation of Personal Data: Perpetrators exploit personal data and images available online, such as social media posts or captured video chats, to create AI- manipulation violates privacy rights and exploits the vulnerability of individuals who trust that their personal information will be used responsibly.

Facilitation of Extortion: AI sextortion often involves perpetrators demanding monetary payments, sexually themed images or videos, or other favours under the threat of releasing manipulated content to the public or to the victims’ friends and family. The realistic nature of deepfake technology increases the effectiveness of these extortion attempts, placing victims under significant emotional and financial pressure.

Amplification of Harm: Perpetrators use deepfake technology to create explicit videos or images that appear realistic, thereby increasing the potential for humiliation, harassment, and psychological trauma suffered by victims. The wide distribution of such content on social media platforms and pornographic websites can perpetuate victimisation and cause lasting damage to their reputation and well-being.

Targeting teenagers– Targeting teenagers and extortion demands in AI sextortion cases is a particularly alarming aspect of this issue. Teenagers are particularly vulnerable to AI sextortion due to their increased use of social media platforms for sharing personal information and images. Perpetrators exploit to manipulate and coerce them.

Erosion of Trust: Misusing AI-driven deepfake technology erodes trust in digital media and online interactions. As deepfake content becomes more convincing, it becomes increasingly challenging to distinguish between real and manipulated videos or images.

Proliferation of Pornographic Content: The misuse of AI technology in sextortion contributes to the proliferation of non-consensual pornography (also known as “revenge porn”) and the availability of explicit content featuring unsuspecting individuals. This perpetuates a culture of objectification, exploitation, and non-consensual sharing of intimate material.

Conclusion

Addressing the concern of AI sextortion requires a multi-faceted approach, including technological advancements in detection and prevention, legal frameworks to hold offenders accountable, awareness about the risks, and collaboration between technology companies, law enforcement agencies, and advocacy groups to combat this emerging threat and protect the well-being of individuals online.

Introduction

The fast-paced development of technology and the wider use of social media platforms have led to the rapid dissemination of misinformation with characteristics such as diffusion, fast propagation speed, wide influence, and deep impact through these platforms. Social Media Algorithms and their decisions are often perceived as a black box introduction that makes it impossible for users to understand and recognise how the decision-making process works. 

Social media algorithms may unintentionally promote false narratives that garner more interactions, further reinforcing the misinformation cycle and making it harder to control its spread within vast, interconnected networks. Algorithms judge the content based on the metrics, which is user engagement. It is the prerequisite for algorithms to serve you the best. Hence, algorithms or search engines enlist relevant items you are more likely to enjoy. This process, initially, was created to cut the clutter and provide you with the best information. However, sometimes it results in unknowingly widespread misinformation due to the viral nature of information and user interactions. 

Analysing the Algorithmic Architecture of Misinformation

Social media algorithms, designed to maximize user engagement, can inadvertently promote misinformation due to their tendency to trigger strong emotions, creating echo chambers and filter bubbles. These algorithms prioritize content based on user behaviour, leading to the promotion of emotionally charged misinformation. Additionally, the algorithms prioritize content that has the potential to go viral, which can lead to the spread of false or misleading content faster than corrections or factual content.

Additionally, popular content is amplified by platforms, which spreads it faster by presenting it to more users. Limited fact-checking efforts are particularly difficult since, by the time they are reported or corrected, erroneous claims may have gained widespread acceptance due to delayed responses. Social media algorithms find it difficult to distinguish between real people and organized networks of troll farms or bots that propagate false information. This creates a vicious loop where users are constantly exposed to inaccurate or misleading material, which strengthens their convictions and disseminates erroneous information through networks.

Though algorithms, primarily, aim to enhance user engagement by curating content that aligns with the user's previous behaviour and preferences. Sometimes this process leads to "echo chambers," where individuals are exposed mainly to information that reaffirms their beliefs which existed prior, effectively silencing dissenting voices and opposing viewpoints. This curated experience reduces exposure to diverse opinions and amplifies biased and polarising content, making it arduous for users to discern credible information from misinformation. Algorithms feed into a feedback loop that continuously gathers data from users' activities across digital platforms, including websites, social media, and apps. This data is analysed to optimise user experiences, making platforms more attractive. While this process drives innovation and improves user satisfaction from a business standpoint, it also poses a danger in the context of misinformation. The repetitive reinforcement of user preferences leads to the entrenchment of false beliefs, as users are less likely to encounter fact-checks or corrective information.

Moreover, social networks and their sheer size and complexity today exacerbate the issue. With billions of users participating in online spaces, misinformation spreads rapidly, and attempting to contain it—such as by inspecting messages or URLs for false information—can be computationally challenging and inefficient. The extensive amount of content that is shared daily means that misinformation can be propagated far quicker than it can get fact-checked or debunked.

Understanding how algorithms influence user behaviour is important to tackling misinformation. The personalisation of content, feedback loops, the complexity of network structures, and the role of superspreaders all work together to create a challenging environment where misinformation thrives. Hence, highlighting the importance of countering misinformation through robust measures. 

The Role of Regulations in Curbing Algorithmic Misinformation

The EU's Digital Services Act (DSA) applicable in the EU is one of the regulations that aims to increase the responsibilities of tech companies and ensure that their algorithms do not promote harmful content. These regulatory frameworks play an important role they can be used to establish mechanisms for users to appeal against the algorithmic decisions and ensure that these systems do not disproportionately suppress legitimate voices. Independent oversight and periodic audits can ensure that algorithms are not biased or used maliciously. Self-regulation and Platform regulation are the first steps that can be taken to regulate misinformation. By fostering a more transparent and accountable ecosystem, regulations help mitigate the negative effects of algorithmic misinformation, thereby protecting the integrity of information that is shared online. In the Indian context, the Intermediary Guidelines, 2023, Rule 3(1)(b)(v) explicitly prohibits the dissemination of misinformation on digital platforms. The ‘Intermediaries’ are obliged to ensure reasonable efforts to prevent users from hosting, displaying, uploading, modifying, publishing, transmitting, storing, updating, or sharing any information related to the 11 listed user harms or prohibited content. This rule aims to ensure platforms identify and swiftly remove misinformation, and false or misleading content.

Cyberpeace Outlook

Understanding how algorithms prioritise content will enable users to critically evaluate the information they encounter and recognise potential biases. Such cognitive defenses can empower individuals to question the sources of the information and report misleading content effectively. In the future of algorithms in information moderation, platforms should evolve toward more transparent, user-driven systems where algorithms are optimised not just for engagement but for accuracy and fairness. Incorporating advanced AI moderation tools, coupled with human oversight can improve the detection and reduction of harmful and misleading content. Collaboration between regulatory bodies, tech companies, and users will help shape the algorithms landscape to promote a healthier, more informed digital environment.

References:

• https://www.advancedsciencenews.com/misformation-spreads-like-a-nuclear-reaction-on-the-internet/
• https://www.niemanlab.org/2024/09/want-to-fight-misinformation-teach-people-how-algorithms-work/ 
• Press Release: Press Information Bureau (pib.gov.in) 

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The World Wide Web was created as a portal for communication, to connect people from far away, and while it started with electronic mail, mail moved to instant messaging, which let people have conversations and interact with each other from afar in real-time. But now, the new paradigm is the Internet of Things and how machines can communicate with one another. Now one can use a wearable gadget that can unlock the front door upon arrival at home and can message the air conditioner so that it switches on. This is IoT.

WHAT EXACTLY IS IoT?

The term ‘Internet of Things’ was coined in 1999 by Kevin Ashton, a computer scientist who put Radio Frequency Identification (RFID) chips on products in order to track them in the supply chain, while he worked at Proctor & Gamble (P&G). And after the launch of the iPhone in 2007, there were already more connected devices than people on the planet.

Fast forward to today and we live in a more connected world than ever. So much so that even our handheld devices and household appliances can now connect and communicate through a vast network that has been built so that data can be transferred and received between devices. There are currently more IoT devices than users in the world and according to the WEF’s report on State of the Connected World, by 2025 there will be more than 40 billion such devices that will record data so it can be analyzed.

IoT finds use in many parts of our lives. It has helped businesses streamline their operations, reduce costs, and improve productivity. IoT also helped during the Covid-19 pandemic, with devices that could help with contact tracing and wearables that could be used for health monitoring. All of these devices are able to gather, store and share data so that it can be analyzed. The information is gathered according to rules set by the people who build these systems.

APPLICATION OF IoT

IoT is used by both consumers and the industry.

Some of the widely used examples of CIoT (Consumer IoT) are wearables like health and fitness trackers, smart rings with near-field communication (NFC), and smartwatches. Smartwatches gather a lot of personal data. Smart clothing, with sensors on it, can monitor the wearer’s vital signs. There are even smart jewelry, which can monitor sleeping patterns and also stress levels.

With the advent of virtual and augmented reality, the gaming industry can now make the experience even more immersive and engrossing. Smart glasses and headsets are used, along with armbands fitted with sensors that can detect the movement of arms and replicate the movement in the game.

At home, there are smart TVs, security cameras, smart bulbs, home control devices, and other IoT-enabled ‘smart’ appliances like coffee makers, that can be turned on through an app, or at a particular time in the morning so that it acts as an alarm. There are also voice-command assistants like Alexa and Siri, and these work with software written by manufacturers that can understand simple instructions.

Industrial IoT (IIoT) mainly uses connected machines for the purposes of synchronization, efficiency, and cost-cutting. For example, smart factories gather and analyze data as the work is being done. Sensors are also used in agriculture to check soil moisture levels, and these then automatically run the irrigation system without the need for human intervention.

Statistics

• The IoT device market is poised to reach $1.4 trillion by 2027, according to Fortune Business Insight.
• The number of cellular IoT connections is expected to reach 3.5 billion by 2023. (Forbes)
• The amount of data generated by IoT devices is expected to reach 73.1 ZB (zettabytes) by 2025.
• 94% of retailers agree that the benefits of implementing IoT outweigh the risk.
• 55% of companies believe that 3^rd party IoT providers should have to comply with IoT security and privacy regulations.
• 53% of all users acknowledge that wearable devices will be vulnerable to data breaches, viruses,
• Companies could invest up to 15 trillion dollars in IoT by 2025 (Gigabit)

CONCERNS AND SOLUTIONS

• Two of the biggest concerns with IoT devices are the privacy of users and the devices being secure in order to prevent attacks by bad actors. This makes knowledge of how these things work absolutely imperative.
• It is worth noting that these devices all work with a central hub, like a smartphone. This means that it pairs with the smartphone through an app and acts as a gateway, which could compromise the smartphone as well if a hacker were to target that IoT device.
• With technology like smart television sets that have cameras and microphones, the major concern is that hackers could hack and take over the functioning of the television as these are not adequately secured by the manufacturer.
• A hacker could control the camera and cyberstalk the victim, and therefore it is very important to become familiar with the features of a device and ensure that it is well protected from any unauthorized usage. Even simple things, like keeping the camera covered when it is not being used.
• There is also the concern that since IoT devices gather and share data without human intervention, they could be transmitting data that the user does not want to share. This is true of health trackers. Users who wear heart and blood pressure monitors have their data sent to the insurance company, who may then decide to raise the premium on their life insurance based on the data they get.
• IoT devices often keep functioning as normal even if they have been compromised. Most devices do not log an attack or alert the user, and changes like higher power or bandwidth usage go unnoticed after the attack. It is therefore very important to make sure the device is properly protected.
• It is also important to keep the software of the device updated as vulnerabilities are found in the code and fixes are provided by the manufacturer. Some IoT devices, however, lack the capability to be patched and are therefore permanently ‘at risk’.

CONCLUSION

Humanity inhabits this world that is made up of all these nodes that talk to each other and get things done. Users can harmonize their devices so that everything runs like a tandem bike – completely in sync with all other parts. But while we make use of all the benefits, it is also very important that one understands what they are using, how it is functioning, and how one can tackle issues should they come up. This is also important to understand because once people get used to IoT, it will be that much more difficult to give up the comfort and ease that these systems provide, and therefore it would make more sense to be prepared for any eventuality. A lot of times, good and sensible usage alone can keep devices safe and services intact. But users should be aware of any issues because forewarned is forearmed.