#FactCheck: Viral Gambhir Video Predates T20 World Cup 2026, Claim Is Misleading

How clothing patterns, movement and real-world conditions can affect computer vision

Introduction

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A person walks in front of an AI camera. The person is clearly visible to us, yet the system may not always identify the person with the same confidence. This sounds strange until we understand one basic fact: a camera records an image, while an AI model interprets that image through patterns learned during training.

This idea became popular through demonstrations such as “The T-Shirt Invisibility Cloak,” where specially designed clothing was shown as a way to interfere with person-detection systems. The wearer does not become physically invisible. The camera still captures the person. Instead, the visual pattern can make a particular AI model less confident or cause an incorrect prediction.

So, how can a piece of clothing affect machine vision? And what does this tell us about the strengths and limitations of AI surveillance?

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How Does an AI Camera See?

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A normal CCTV camera mainly captures and records video. An AI camera adds software that analyses the video for a specific task, such as detecting a person, vehicle, face or event. 

A simple way to understand the process is:

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The model does not understand a person exactly as a human does. It processes numerical representations learned from training data. For a person detector, the question is closer to “Do these visual patterns match the class ‘person’?” than “I know this is a human.”

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Think About It

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A human sees a person wearing a complex shirt and easily separates the shirt design from the person. An AI model must decide what the visual patterns in the image mean for its particular task.

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When Clothing Becomes Difficult

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Clothing can become challenging when it changes the visual information available to a model.

Complex patterns can add many edges, repeated shapes and textures to an image. These do not automatically confuse an AI camera, but they can influence the features a detector uses.

Low contrast can create another problem. If clothing is close in colour or brightness to the background, the boundary between the person and the environment may be harder to separate, especially in poor lighting or low-resolution footage.

Movement creates another challenge. A loose jacket, flowing fabric or layered clothing changes shape as the person walks or turns. Researchers designing adversarial T-shirts have had to account for these non-rigid changes because a printed pattern does not stay flat on a moving body.

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The T-Shirt Invisibility Cloak

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The video “The T-Shirt Invisibility Cloak” is a simple introduction to a larger research area called physical adversarial examples.

In adversarial machine learning, researchers study inputs deliberately designed to make a model produce an incorrect prediction. With clothing, the visual pattern is physically printed on a garment and then captured by a camera.

A 2019 study by Xu and colleagues demonstrated an adversarial T-shirt designed to interfere with person detectors in the physical world. Under their test conditions, the reported physical attack success rate against YOLOv2 was 57%. The work also modelled cloth deformation caused by movement.

A 2022 CVPR study on adversarial texture extended the idea to different viewing angles and tested physical clothing such as T-shirts, skirts and dresses.

But there is an important limitation: an adversarial garment is not a universal invisibility cloak. Different cameras and AI systems use different models, training data and processing pipelines. A pattern that affects one detector may have little effect on another.

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Why Does the AI Get It Wrong?

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Clothing may be only one part of the problem. A model can receive features different from what it learned during training. Camera angle, distance, movement, lighting, motion blur, compression and occlusion can also change the image.

Imagine the same person in four situations:

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There may not be one fixed answer. AI performance depends on the combination of conditions.

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AI Camera vs Human Vision

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Humans use context. We understand that clothes wrinkle, people turn, shadows change and objects may temporarily block part of a body. An AI detector is more task-specific and can become less reliable when input conditions differ from the data used to train or evaluate it.

This does not make AI useless. It makes realistic testing important.

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Building More Reliable AI Surveillance

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The lesson is not that AI cameras should be abandoned. They should be tested under difficult, realistic conditions.

Testing can include different clothing patterns, colours, body positions, distances, camera angles, lighting conditions and crowded scenes. Security teams should also distinguish between person detection, facial recognition, tracking and behaviour analysis because these are different tasks. ^[6]

Most importantly, an automated alert should be treated as a prediction, not unquestionable truth. Important decisions should include appropriate human review.

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A New Research Direction

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Research is also moving beyond ordinary visible-light cameras. A 2026 CVPR paper explored adversarial clothing designed to affect both visible and infrared surveillance systems. At the same time, researchers continue to study ways of making models more robust against physical adversarial attacks.

This creates a continuing security cycle:

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That cycle is a normal part of security research.

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At a Glance: Human Vision vs AI Detection

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Conclusion

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The idea of an “invisible T-shirt” is fascinating because it challenges a common assumption about AI: if a machine can see an image, we expect it to understand that image correctly.

A person can remain completely visible while a particular AI detector becomes less confident or makes a wrong prediction. Clothing patterns, texture, folds, movement, lighting, camera angle and other factors can interact with a model in unexpected ways. Research has demonstrated physical adversarial clothing against person detectors, while newer work is exploring more complex surveillance conditions.

The real lesson is not that a shirt can make someone invisible. AI vision is a prediction system with strengths and weaknesses. Understanding those weaknesses helps researchers build better defences, helps security teams evaluate systems realistically, and reminds us that automated surveillance should be deployed with technical care, human oversight and respect for privacy.

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References

• The T-Shirt Invisibility Cloak
• YouTube video referenced for the article’s introductory example: https://www.youtube.com/watch?v=NyofHyRm5CQ
• Xu, K., Zhang, G., Liu, S., Fan, Q., Sun, M., Chen, H., Chen, P.-Y., Wang, Y., & Lin, X. (2020). Adversarial T-shirt! Evading Person Detectors in a Physical World. Computer Vision – ECCV 2020, 665–681. https://doi.org/10.1007/978-3-030-58558-7_39
• Study demonstrating a physical adversarial T-shirt against person detection and modelling non-rigid cloth deformation during movement. https://arxiv.org/abs/1910.11099
• Thys, S., Van Ranst, W., & Goedemé, T. (2019). Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection. CVPR Workshops.
• Earlier work showing physical adversarial patches can reduce the accuracy of person detectors. https://openaccess.thecvf.com/content_CVPRW_2019/html/CV-COPS/Thys_Fooling_Automated_Surveillance_Cameras_Adversarial_Patches_to_Attack_Person_Detection_CVPRW_2019_paper.html
• Hu, Z., Huang, S., Zhu, X., Sun, F., Zhang, B., & Hu, X. (2022). Adversarial Texture for Fooling Person Detectors in the Physical World. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13307–13316.
• Study extending physical adversarial clothing to multiple viewing angles and garments including T-shirts, skirts and dresses. https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html
• Long, J., Jiang, T., Liu, H., Ma, C., Zhou, W., Yang, Y., & Yao, W. (2026). Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems. CVPR 2026.
• Recent work exploring adversarial clothing against visible and infrared surveillance systems. https://openaccess.thecvf.com/content/CVPR2026/html/Long_Thermally_Activated_Dual-Modal_Adversarial_Clothing_against_AI_Surveillance_Systems_CVPR_2026_paper.html
• Li, S., Zhang, S., Chen, G., Wang, D., Feng, P., Wang, J., Liu, A., Yi, X., & Liu, X. (2023). Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12324–12333.
• Useful context on physical adversarial attacks and the challenge of making them visually natural. https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Towards_Benchmarking_and_Assessing_Visual_Naturalness_of_Physical_World_Adversarial_CVPR_2023_paper.pdf ^[6]

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Introduction

Two videos are being widely circulated on social media with the claim that Chinese troops have entered Arunachal Pradesh, India. The first video shows a large group of soldiers marching in formation, while the second shows armed personnel disembarking from military trucks. Social media users claim that the footage shows Chinese soldiers advancing into Indian territory. CyberPeace Research Wing’s research found the claim to be false. The two viral videos are unrelated to India and actually show Thai Army personnel during military training exercises. Both the Government of India and the Indian Army have also dismissed the claim of any recent Chinese intrusion into Indian territory.

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Claim

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An Instagram user, "mtkv.ideo," shared the viral videos on July 7, 2026, with the caption,"Chinese army has entered 60 km inside India."

https://www.instagram.com/reels/DaehrgExzUl/

https://perma.cc/ZQV9-C6KJ 

Fact Check

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To verify the claim, we extracted several keyframes from the viral videos and conducted a reverse image search using Google Lens.

During the research, we found one of the videos posted on June 16, 2026, on the Facebook page "Liangpha Kha Khaeng."

https://www.facebook.com/reel/992963226961409 

https://perma.cc/6YJB-RT9Z

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According to the information provided with the post, the video shows a training exercise of Non-Commissioned Officers (NCOs) from the 3rd Battalion, 2nd Regiment of the Royal Thai Army. The page also contains several other posts featuring Thai military drills and training activities. We further reviewed the Facebook page and found multiple training videos featuring the same flag carried by soldiers in the viral footage, confirming that the personnel shown belong to the Thai Army, not the Chinese military.

https://www.facebook.com/profile.php?id=61588894033585&locale=hi_IN

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We also found a report published by The Times of India, quoting Union Minister Kiren Rijiju, who dismissed claims of any Chinese intrusion into Arunachal Pradesh. Rijiju clarified that no intrusion had taken place, adding that occasional movement across undemarcated areas occurs because the boundary has not been fully delineated. https://timesofindia.indiatimes.com/india/no-intrusion-transgression-in-absence-of-demarcation-kiran-rijiju/articleshow/132101410.cms

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Conclusion

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Our research found that the viral claim is false. The videos being shared as evidence of Chinese troops entering Arunachal Pradesh actually show Royal Thai Army personnel participating in military training exercises in Thailand. There is no evidence that the footage is related to India or that it depicts any recent Chinese military intrusion into Indian territory. Government officials have also rejected the viral claim.

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According to Statista, the number of users in India's digital assets market is expected to reach 107.30m users by 2025 (Impacts of Inflation on Financial Markets, August 2023). India's digital asset market has been experiencing exponential growth fueled by the increased adoption of cryptocurrencies and blockchain technology. This furthers the need for its regulation. Digital assets include cryptocurrencies, NFTs, asset-backed tokens, and tokenised real estate. 

India has defined Digital Assets under Section 47(A) of the Income Tax Act, 1961. The Finance Act 2022-23 has added the word 'virtual' to make it “Virtual Digital Assets”. A “virtual digital asset” is any information or code, number, or token, created through cryptographic methods or otherwise, by any name, giving a digital representation of value exchanged with or without consideration. A VDA should contain an inherent value and represent a store of value or unit of account, functional in any financial transaction or investment. These can be stored,  transferred, or traded in electronic format. 

Digital Asset Governance: Update and Future Outlook

Indian regulators have been conservative in their approach towards digital assets, with the Reserve Bank of India first issuing directions against cryptocurrency transactions in 2018. This ban was removed by the Supreme Court through a court order in 2020. The presentation of the Cryptocurrency and Regulation of Official Digital Currency Bill of 2021 is a fairly important milestone in its attempts to lay down the framework for issuing an official digital currency by the Reserve Bank of India. While some digital assets seem to have potential, like the Central Bank Digital Currencies (CBDCs) and blockchain-based financial applications, a blanket prohibition has been enforced on private cryptocurrencies.

However, in more recent trends, the landscape is changing as the RBI's CBDC is to provide a state-backed digital alternative to cash under a more structured regulatory framework. This move seeks to balance state control with innovation on investor safety and compliance, expecting to reduce risk and enhance security for investors by enacting strict anti-money laundering and know-your-customer laws. Highlighting these developments is important to examine how global regulatory trends influence India's digital asset policies.

Impact of Global Development on India’s Approach 

Global regulatory developments have an impact on Indian policies on digital assets. The European Union's Markets in Crypto-assets (MiCA) is to introduce a comprehensive regulatory framework for cryptocurrencies that could act as an inspiration for India. MiCA regulation covers crypto-assets that are not currently regulated by existing financial services legislation. Its particular focus on consumer protection and market integrity resonates with India in terms of investigating needs related to digital assets, including fraud and price volatility. Additionally, evolving policies in the US, such as regulating crypto exchanges and classifying certain tokens as securities, could also form the basis for India's regulatory posture.

Collaboration on the international level is also a chief contributing factor. India’s regular participation in global forums like the G20, facilitates an opportunity to align its regulations on digital assets with other countries, tending toward an even more standardised and predictable framework for cross-border transactions. This can significantly help India given that the nation  has a huge diaspora providing a critical inflow of remuneration.

CyberPeace Outlook

Though digital assets offer many opportunities to India, challenges also exist. Cryptocurrency volatility affects investors, posing concerns over fraud and illicit dealings. A balance between the need for innovation and investor protection is paramount to avoid killing the growth of India's digital asset ecosystem with overly restrictive regulations.

Financial inclusion, efficient cross-border payments with low transaction costs, and the opening of investment opportunities are a few opportunities offered by digital assets. For example, the tokenisation of real estate throws open real estate investment to smaller investors. To strengthen the opportunities while addressing challenges, some policy reforms and new frameworks might prove beneficial. 

CyberPeace Policy Recommendations

• Establish a regulatory sandbox for startups working in the area of blockchain and digital assets. This would allow them to test innovative solutions in a controlled environment with regulatory oversight minimising risks.
• Clear guidelines for the taxation of digital assets should be provided as they will ensure transparency, reduce ambiguity for investors, and promote compliance with tax regulations. Specific guidelines can be drawn from the EU's MiCA regulation.  
• Workshops, online resources, and campaigns are some examples of initiatives aimed at improving consumer awareness about digital assets, benefits and associated risks that should be implemented. Partnerships with global fintech firms will provide a great opportunity  to learn best practices.

Conclusion

India is positioned at a critical juncture with respect to the debate on digital assets. The challenge which lies ahead is one of balancing innovation with effective regulation. The introduction of the Central Bank Digital Currency (CBDC) and the development of new policies signal a willingness on the part of the regulators to embrace the digital future. In contrast, issues like volatility, fraud, and regulatory compliance continue to pose hurdles. By drawing insights from global frameworks and strengthening ties through international forums, India can pave the way for a secure and dynamic digital asset ecosystem. Embracing strategic measures such as regulatory sandboxes and transparent tax guidelines will not only protect investors but also unlock the immense potential of digital assets, propelling India into a new era of financial innovation and inclusivity.

References

• https://www.weforum.org/agenda/2024/10/different-countries-navigating-uncertainty-digital-asset-regulation-election-year/ 
• https://www.acfcs.org/eu-passes-landmark-crypto-regulation 
• https://www.indiabudget.gov.in/budget2022-23/doc/Finance_Bill.pdf 
• https://www.weforum.org/agenda/2024/10/different-countries-navigating-uncertainty-digital-asset-regulation-election-year/
• https://www3.weforum.org/docs/WEF_Digital_Assets_Regulation_2024.pdf 

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