#FactCheck -Viral Post Falsely Attributes Communal NEET Remark to Kangana Ranaut; Fact Check Debunks Claim

Introduction

In a world where Artificial Intelligence (AI) is already changing the creation and consumption of content at a breathtaking pace, distinguishing between genuine media and false or doctored content is a serious issue of international concern. AI-generated content in the form of deepfakes, synthetic text and photorealistic images is being used to disseminate misinformation, shape public opinion and commit fraud. As a response, governments, tech companies and regulatory bodies are exploring ‘watermarking’ as a key mechanism to promote transparency and accountability in AI-generated media. Watermarking embeds identifiable information into content to indicate its artificial origin. 

Government Strategies Worldwide

Governments worldwide have pursued different strategies to address AI-generated media through watermarking standards. In the US, President Biden's 2023 Executive Order on AI directed the Department of Commerce and the National Institute of Standards and Technology (NIST) to establish clear guidelines for digital watermarking of AI-generated content. This action puts a big responsibility on large technology firms to put identifiers in media produced by generative models. These identifiers should help fight misinformation and address digital trust.

The European Union, in its Artificial Intelligence Act of 2024, requires AI-generated content to be labelled. Article 50 of the Act specifically demands that developers indicate whenever users engage with synthetic content. In addition, the EU is a proponent of the Coalition for Content Provenance and Authenticity (C2PA), an organisation that produces secure metadata standards to track the origin and changes of digital content.

India is currently in the process of developing policy frameworks to address AI and synthetic content, guided by judicial decisions that are helping shape the approach. In 2024, the Delhi High Court directed the central government to appoint members for a committee responsible for regulating deepfakes. Such moves indicate the government's willingness to regulate AI-generated content. 

China, has already implemented mandatory watermarking on all deep synthesis content. Digital identifiers must be embedded in AI media by service providers, and China is one of the first countries to adopt stern watermarking legislation.

Understanding the Technical Feasibility

Watermarking AI media means inserting recognisable markers into digital material. They can be perceptible, such as logos or overlays or imperceptible, such as cryptographic tags or metadata. Sophisticated methods such as Google's SynthID apply imperceptible pixel-level changes that remain intact against standard image manipulation such as resizing or compression. Likewise, C2PA metadata standards enable the user to track the source and provenance of an item of content.

Nonetheless, watermarking is not an infallible process. Most watermarking methods are susceptible to tampering. Aforementioned adversaries with expertise, for instance, can use cropping editing or AI software to delete visible watermarks or remove metadata. Further, the absence of interoperability between different watermarking systems and platforms hampers their effectiveness. Scalability is also an issue enacting and authenticating watermarks for billions of units of online content necessitates huge computational efforts and routine policy enforcement across platforms. Scientists are currently working on solutions such as blockchain-based content authentication and zero-knowledge watermarking, which maintain authenticity without sacrificing privacy. These new techniques have potential for overcoming technical deficiencies and making watermarking more secure.

Challenges in Enforcement

Though increasing agreement exists for watermarking, implementation of such policies is still a major issue. Jurisdictional constraints prevent enforceability globally. A watermarking policy within one nation might not extend to content created or stored in another, particularly across decentralised or anonymous domains. This creates an exigency for international coordination and the development of worldwide digital trust standards. While it is a welcome step that platforms like Meta, YouTube, and TikTok have begun flagging AI-generated content, there remains a pressing need for a standardised policy that ensures consistency and accountability across all platforms. Voluntary compliance alone is insufficient without clear global mandates.

User literacy is also a significant hurdle. Even when content is properly watermarked, users might not see or comprehend its meaning. This aligns with issues of dealing with misinformation, wherein it's not sufficient just to mark off fake content, users need to be taught how to think critically about the information they're using. Public education campaigns, digital media literacy and embedding watermarking labels within user-friendly UI elements are necessary to ensure this technology is actually effective.

Balancing Privacy and Transparency

While watermarking serves to achieve digital transparency, it also presents privacy issues. In certain instances, watermarking might necessitate the embedding of metadata that will disclose the source or identity of the content producer. This threatens journalists, whistleblowers, activists, and artists utilising AI tools for creative or informative reasons. Governments have a responsibility to ensure that watermarking norms do not violate freedom of expression or facilitate surveillance. The solution is to achieve a balance by employing privacy-protection watermarking strategies that verify the origin of the content without revealing personally identifiable data. "Zero-knowledge proofs" in cryptography may assist in creating watermarking systems that guarantee authentication without undermining user anonymity.

On the transparency side, watermarking can be an effective antidote to misinformation and manipulation. For example, during the COVID-19 crisis, misinformation spread by AI on vaccines, treatments and public health interventions caused widespread impact on public behaviour and policy uptake. Watermarked content would have helped distinguish between authentic sources and manipulated media and protected public health efforts accordingly.

Best Practices and Emerging Solutions

Several programs and frameworks are at the forefront of watermarking norms. Adobe, Microsoft and others' collaborative C2PA framework puts tamper-proof metadata into images and videos, enabling complete traceability of content origin. SynthID from Google is already implemented on its Imagen text-to-image model and secretly watermarks images generated by AI without any susceptibility to tampering. The Partnership on AI (PAI) is also taking a leadership role by building out ethical standards for synthetic content, including standards around provenance and watermarking. These frameworks become guides for governments seeking to introduce equitable, effective policies. In addition, India's new legal mechanisms on misinformation and deepfake regulation present a timely point to integrate watermarking standards consistent with global practices while safeguarding civil liberties.

Conclusion

Watermarking regulations for synthetic media content are an essential step toward creating a safer and more credible digital world. As artificial media becomes increasingly indistinguishable from authentic content, the demand for transparency, origin, and responsibility increases. Governments, platforms, and civil society organisations will have to collaborate to deploy watermarking mechanisms that are technically feasible, compliant and privacy-friendly. India is especially at a turning point, with courts calling for action and regulatory agencies starting to take on the challenge. Empowering themselves with global lessons, applying best-in-class watermarking platforms and promoting public awareness can enable the nation to acquire a level of resilience against digital deception. 

References

1. https://artificialintelligenceact.eu/
2. https://www.cyberpeace.org/resources/blogs/delhi-high-court-directs-centre-to-nominate-members-for-deepfake-committee
3.  https://c2pa.org
4. https://www.cyberpeace.org/resources/blogs/misinformations-impact-on-public-health-policy-decisions
5.  https://deepmind.google/technologies/synthid/
6. https://www.imatag.com/blog/china-regulates-ai-generated-content-towards-a-new-global-standard-for-transparency

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Executive Summary
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A video is going viral on social media showing a massive building engulfed in flames and collapsing into debris. It is being widely claimed that Iran launched a powerful attack that destroyed Israel’s army headquarters. However, research by CyberPeace reveals that this claim is misleading. The viral video is AI-generated and has no connection to any real-world event.

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Claim

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An X (formerly Twitter) user shared the viral video with the caption: “Iran has targeted Israel’s army headquarters. It seems Israel’s dream of becoming ‘Greater Israel’ will remain unfulfilled.”

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Post link:

• https://x.com/KAMESHKUMAR96/status/2039009484069368083

Archived version:

• https://archive.ph/HKXkK
• https://x.com/KAMESHKUMAR96/status/2039009484069368083 
• https://archive.ph/HKXkK 

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Similar videos have also been shared by other users on social media:

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• https://www.facebook.com/reel/913431111396693

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Fact Check

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To verify the claim, we extracted keyframes from the viral video and conducted a reverse image search. During this process, we found several credible media reports confirming that Iran has carried out drone and missile attacks on Israel and the Gulf regions in recent times. However, none of these reports featured the viral video, indicating that it is not authentic footage.

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https://www.aljazeera.com/video/newsfeed/2026/4/1/central-israel-hit-by-one-of-irans-largest-missile-barrages-in-weeks

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• https://www.youtube.com/watch?v=fxDBX90bYng

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A closer examination of the video revealed multiple visual inconsistencies commonly associated with AI-generated content. For instance, a building on the left side appears to bend and collapse in a rubber-like manner—something that is physically unrealistic for structures made of concrete and steel. Additionally, the smoke and flames appear unnatural and lack realistic dynamics.

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To further verify, we analyzed the video using the AI detection tool Hive Moderation, which classified it as 99.9% AI-generated.

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We also tested the video using the Deepfake-o-Meter platform.The AVSRDD (2025) model detected it as 99.5% AI-generated

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Conclusion

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Our research clearly establishes that the viral video claiming Iran destroyed Israel’s army headquarters is false and misleading. The footage does not appear in any credible news coverage of recent attacks, which strongly indicates that it is not real. Moreover, multiple AI detection tools consistently classify the video as artificially generated, with extremely high probability scores. Visual anomalies in the clip further support this finding.

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Introduction

Microsoft has unveiled its ambitious roadmap for developing a quantum supercomputer with AI features, acknowledging the transformative power of quantum computing in solving complex societal challenges. Quantum computing has the potential to revolutionise AI by enhancing its capabilities and enabling breakthroughs in different fields. Microsoft’s groundbreaking announcement of its plans to develop a quantum supercomputer, its potential applications, and the implications for the future of artificial intelligence (AI). However, there is a need for regulation in the realms of quantum computing and AI and significant policies and considerations associated with these transformative technologies. This technological advancement will help in the successful development and deployment of quantum computing, along with the potential benefits and challenges associated with its implementation.

What isQuantum computing?

Quantum computing is an emerging field of computer science and technology that utilises principles from quantum mechanics to perform complex calculations and solve certain types of problems more efficiently than classical computers. While classical computers store and process information using bits, quantum computers use quantum bits or qubits.

‍Interconnected Future  

Quantum computing promises to significantly expand AI’s capabilities beyond its current limitations. Integrating these two technologies could lead to profound advancements in various sectors, including healthcare, finance, and cybersecurity. Quantum computing and artificial intelligence (AI) are two rapidly evolving fields that have the potential to revolutionise technology and reshape various industries. This section explores the interdependence of quantum computing and AI, highlighting how integrating these two technologies could lead to profound advancements across sectors such as healthcare, finance, and cybersecurity.

1. Enhancing AI Capabilities:

Quantum computing holds the promise of significantly expanding the capabilities of AI systems. Traditional computers, based on classical physics and binary logic, need help solving complex problems due to the exponential growth of computational requirements. Quantum computing, on the other hand, leverages the principles of quantum mechanics to perform computations on quantum bits or qubits, which can exist in multiple states simultaneously. This inherent parallelism and superposition property of qubits could potentially accelerate AI algorithms and enable more efficient processing of vast amounts of data.

1. Solving Complex Problems:

The integration of quantum computing and AI has the potential to tackle complex problems that are currently beyond the reach of classical computing methods. Quantum machine learning algorithms, for example, could leverage quantum superposition and entanglement to analyse and classify large datasets more effectively. This could have significant applications in healthcare, where AI-powered quantum systems could aid in drug discovery, disease diagnosis, and personalised medicine by processing vast amounts of genomic and clinical data.

1. Advancements in Finance and Optimisation:

The financial sector can benefit significantly from integrating quantum computing and AI. Quantum algorithms can be employed to optimise portfolios, improve risk analysis models, and enhance trading strategies. By harnessing the power of quantum machine learning, financial institutions can make more accurate predictions and informed decisions, leading to increased efficiency and reduced risks.

1. Strengthening Cybersecurity:

Quantum computing can also play a pivotal role in bolstering cybersecurity defences. Quantum techniques can be employed to develop new cryptographic protocols that are resistant to quantum attacks. In conjunction with quantum computing, AI can further enhance cybersecurity by analysing massive amounts of network traffic and identifying potential vulnerabilities or anomalies in real time, enabling proactive threat mitigation.

1. Quantum-Inspired AI:

Beyond the direct integration of quantum computing and AI, quantum-inspired algorithms are also being explored. These algorithms, designed to run on classical computers, draw inspiration from quantum principles and can improve performance in specific AI tasks. Quantum-inspired optimisation algorithms, for instance, can help solve complex optimisation problems more efficiently, enabling better resource allocation, supply chain management, and scheduling in various industries.

How Quantum Computing and AI Should be Regulated-

As quantum computing and artificial intelligence (AI) continues to advance, questions arise regarding the need for regulations to govern these technologies. There is debate surrounding the regulation of quantum computing and AI, considering the potential risks, ethical implications, and the balance between innovation and societal protection.

1. Assessing Potential Risks: Quantum computing and AI bring unprecedented capabilities that can significantly impact various aspects of society. However, they also pose potential risks, such as unintended consequences, privacy breaches, and algorithmic biases. Regulation can help identify and mitigate these risks, ensuring these technologies’ responsible development and deployment.
2. Ethical Implications: AI and quantum computing raise ethical concerns related to privacy, bias, accountability, and the impact on human autonomy. For AI, issues such as algorithmic fairness, transparency, and decision-making accountability must be addressed. Quantum computing, with its potential to break current encryption methods, requires regulatory measures to protect sensitive information. Ethical guidelines and regulations can provide a framework to address these concerns and promote responsible innovation.
3. Balancing Innovation and Regulation: Regulating quantum computing and AI involves balancing fostering innovation and protecting society’s interests. Excessive regulation could stifle technological advancements, hinder research, and impede economic growth. On the other hand, a lack of regulation may lead to the proliferation of unsafe or unethical applications. A thoughtful and adaptive regulatory approach is necessary, considering the dynamic nature of these technologies and allowing for iterative improvements based on evolving understanding and risks.
4. International Collaboration: Given the global nature of quantum computing and AI, international collaboration in regulation is essential. Harmonising regulatory frameworks can avoid fragmented approaches, ensure consistency, and facilitate ethical and responsible practices across borders. Collaborative efforts can also address data privacy, security, and cross-border data flow challenges, enabling a more unified and cooperative approach towards regulation.
5. Regulatory Strategies: Regulatory strategies for quantum computing and AI should adopt a multidisciplinary approach involving stakeholders from academia, industry, policymakers, and the public. Key considerations include:

1. Risk-based Approach: Regulations should focus on high-risk applications while allowing low-risk experimentation and development space.
2. Transparency and Explainability: AI systems should be transparent and explainable to enable accountability and address concerns about bias, discrimination, and decision-making processes.
3. Privacy Protection: Regulations should safeguard individual privacy rights, especially in quantum computing, where current encryption methods may be vulnerable.
4. Testing and Certification: Establishing standards for the testing and certification of AI systems can ensure their reliability, safety, and adherence to ethical principles.
5. Continuous Monitoring and Adaptation: Regulatory frameworks should be dynamic, regularly reviewed, and adapted to keep pace with the evolving landscape of quantum computing and AI.

Conclusion:

‍Integrating quantum computing and AI holds immense potential for advancing technology across diverse domains. Quantum computing can enhance the capabilities of AI systems, enabling the solution of complex problems, accelerating data processing, and revolutionising industries such as healthcare, finance, and cybersecurity. As research and development in these fields progress, collaborative efforts among researchers, industry experts, and policymakers will be crucial in harnessing the synergies between quantum computing and AI to drive innovation and shape a transformative future.The regulation of quantum computing and AI is a complex and ongoing discussion. Striking the right balance between fostering innovation, protecting societal interests, and addressing ethical concerns is crucial. A collaborative, multidisciplinary approach to regulation, considering international cooperation, risk assessment, transparency, privacy protection, and continuous monitoring, is necessary to ensure these transformative technologies' responsible development and deployment.