#FactCheck -Viral Post Falsely Attributes Communal NEET Remark to Kangana Ranaut; Fact Check Debunks Claim
Executive Summary
A post claiming to be a statement by BJP MP Kangana Ranaut regarding the NEET paper leak is going viral on social media. The post allegedly quotes her as saying:“Hindus are in danger here and you are worried about the NEET exam. If Hindus do not exist, who will take the NEET exam?” The CyberPeace Research Wing research found this claim to be fake. Kangana Ranaut herself has also denied the viral post through her official X (formerly Twitter) account.
Claim
A user on X shared the viral graphic and wrote that Hindus are in danger and questioned the relevance of the NEET exam, further linking it to political criticism of the BJP government.
Fact Check
During the research, keyword-based searches revealed no credible reports linking Kangana Ranaut to any such statement regarding NEET paper leaks or Hindus. We also reviewed Kangana Ranaut’s official social media accounts. On May 20, 2026, she tagged Congress leader Surendra Singh Rajput in an X post and clearly termed the viral statement as fake. She also criticized Rajput and the Congress party over the spread of misinformation. Notably, Surendra Rajput later deleted his original post.
On May 21, Rajput reposted Kangana’s clarification, stating that after her denial it was clear that the poster and statement were not hers. He also said he had deleted his post. Under his post, a user shared screenshots of the deleted content.
Conclusion
Our research confirms that Kangana Ranaut has not made any such statement related to the NEET paper leak or Hindus. The viral claim is fake.
Related Blogs
How clothing patterns, movement and real-world conditions can affect computer vision
Introduction
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?
How Does an AI Camera See?
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:
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.”
Think About It
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.
When Clothing Becomes Difficult
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.
The T-Shirt Invisibility Cloak
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.
Why Does the AI Get It Wrong?
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:
There may not be one fixed answer. AI performance depends on the combination of conditions.
AI Camera vs Human Vision
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.
Building More Reliable AI Surveillance
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.
A New Research Direction
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:
That cycle is a normal part of security research.
At a Glance: Human Vision vs AI Detection
Conclusion
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.
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
India's broadcasting sector has undergone significant changes in recent years with technological advancements such as the introduction of new platforms like Direct-to-Home (DTH), Internet Protocol television (IPTV), Over-The-Top (OTT), and integrated models. Platform changes, emerging technologies and advancements in the advertising space have all necessitated the need for new governing laws that take these developments into account.
The Union Government and concerned ministry have realised there is a pressing need to develop a robust regulatory framework for the Indian broadcasting sector in the country and consequently, a draft Broadcasting Services (Regulation) Bill, 2023, was released in November 2023 and the Union Ministry of Information and Broadcasting (MIB) had invited feedback and comments from different stakeholders. The draft Bill aims to establish a unified framework for regulating broadcasting services in the country, replacing the current Cable Television Networks (Regulation) Act, 1995 and other policy guidelines governing broadcasting.
Recently a new draft of an updated ‘Broadcasting Services (Regulation) Bill, 2024,’ was shared with selected broadcasters, associations, streaming services, and tech firms, each marked with their identifier to prevent leaks.
Key Highlights of the Updated Broadcasting Bill
As per the recent draft of the Broadcasting Services (Regulation) Bill, 2024, social media accounts could be identified as ‘Digital News Broadcasters’ and can be classified within the ambit of the regulation. Some of the major aspects of the new bill were first reported by Hindustan Times.
The new draft of the Broadcasting Services (Regulation) Bill, 2024, proposes that individuals who regularly upload videos to social media, make podcasts, or write about current affairs online could be classified as Digital News Broadcasters. This entails that YouTubers and Instagrammers who receive a share of advertising revenue or monetize their social media presence through affiliate activities will be regulated as Digital News Broadcasters. This includes channels, podcasts, and blogs that cover news and utilise Google AdSense. They must comply with a Programme Code and Advertising Code.
Online content creators who do not provide news or current affairs but provide programming and curated programs beyond a certain threshold will be treated as OTT broadcasters in case they provide content licensed or live through a website or social media platform.
The new version also introduces new obligations for intermediaries and social media intermediaries related to streaming services and digital news broadcasters, and, in contrast to the last version circulated in 2023, the latest also carries provisions targeting online advertising. In the context of streaming services, OTT broadcasting services are no longer a part of the definition of "internet broadcasting services." The definition of OTT broadcasting service has also been revised, allowing content creators who regularly upload their content to social media to be considered as OTT broadcasting services.
The new definition of an 'intermediary' includes social media intermediaries, advertisement intermediaries, internet service providers, online search engines, and online marketplaces.
The new Bill allows the government to prescribe different due diligence guidelines for social media platforms and online advertisement intermediaries and requires all intermediaries to provide appropriate information, including information pertaining to the OTT broadcasters and Digital News Broadcasters on their platform, to the central government to ensure compliance with the act. This entails the liability provisions for social media intermediaries which do not provide information “pertaining to OTT Broadcasters and Digital News Broadcasters” on its platforms for compliance. This suggests that when information is sought about a YouTube, Instagram or X/Twitter user, the platform will need to provide this information to the Indian government.
A new draft bill contains specific provisions governing ‘Online Advertising’ and to do so it creates the category of 'advertising intermediaries'. These intermediaries enable the buying or selling of advertisement space on the internet or placing advertisements on online platforms without endorsing the advertisement.
Final Words
The Indian Ministry of Information and Broadcasting (MIB) is making efforts to propose robust regulatory changes to the country's new-age broadcast sector, which would cover the specific provisions for Digital News Broadcasters, OTT Broadcasters and Intermediaries. The proposed bill defining the scope and obligation of each.
However, these changes will have significant implications for press and creative freedom. The changes in the new version of the updated bill from its previous draft expanded the applicability of the bill to a larger number of key actors, this move brought ‘content creators’ under the definition of OTT or digital news broadcasters, which raises concerns about overly rigid provisions and might face criticism from media representative perspectives.
According to recent media reports, the Broadcasting Services (Regulation) Bill, 2024 version has been withdrawn by the I&B ministry facing criticism from relevant stakeholders.
The ministry must take due consideration and feedback from concerned stakeholders and place reliance on balancing individual rights while promoting a healthy regulated landscape considering the needs of the new-age broadcasting sector.
References:
- https://www.medianama.com/2024/07/223-india-broadcast-bill-online-creators/#:~:text=Online%20content%20creators%20that%20do,or%20a%20social%20media%20platform.
- https://www.hindustantimes.com/india-news/new-draft-of-broadcasting-bill-news-influencers-may-be-classified-as-broadcasters-101721961764666.html
- https://www.hindustantimes.com/india-news/broadcasting-bill-still-in-drafting-stage-mib-tells-rs-101722058753083.html
- https://www.newslaundry.com/2024/07/29/indias-new-broadcast-bill-now-has-compliance-requirements-for-youtubers-and-instagrammers
- https://m.thewire.in/article/media/social-media-videos-text-digital-news-broadcasting-bill
- https://mib.gov.in/sites/default/files/Public%20Notice_07.12.2023.pdf
- https://news.abplive.com/news/india/centre-withdraws-draft-of-broadcasting-services-regulation-bill-1709770
Executive Summary
Amid nationwide outrage over the gang-rape of a 13-year-old minor in Sriganganagar, Rajasthan, a video is circulating on social media. The clip shows police officers tying the hands of several accused individuals and parading them publicly on the streets. Users sharing the video claim that these are the culprits from the Sriganganagar case being beaten by the police.
A fact-check by the CyberPeace Research Wing found this claim to be misleading. The viral video has no connection to the Rajasthan incident; it actually depicts a separate event from Patan, Gujarat, dating back to March 2026.
The Claim
On July 7, 2026, a user on X (formerly Twitter) shared the video with the caption: "32 jihadis assaulted, brutalized, and raped a 13-year-old girl for five days. These criminals must get the harshest punishment to set an example."
https://x.com/RakeshKishore_l/status/2074400280352084129
Fact Check
To verify the viral claim, keyframes from the video were extracted and analysed using Google Lens for reverse image search. During the research, we found a report published by NDTV on March 19, 2026, which carried visuals matching the viral video. The report link and screenshot are provided below:
According to the NDTV report, a video showing police personnel publicly assaulting 18 alleged accused persons in Gujarat’s Patan district had surfaced. The incident was reported to have taken place during a crime scene reconstruction exercise.
The report stated that the accused, allegedly linked to an extortion gang, were taken to Chanasma for the reconstruction process. During the exercise, police personnel were seen assaulting them. Witnesses claimed that the accused were paraded through areas where they had allegedly created fear among locals.
The accused were reportedly linked to the alleged Jhiliya gang. The incident occurred days after a violent clash in which members of the same group allegedly attacked a police team, forcing officers to retreat from the spot.
During further research, we found a report published by ABP Asmita on March 19, 2026. According to the report, the incident was linked to an old dispute over temple donations.
The report stated that the accused were produced before a court, which sent them to five days of police custody until March 23, 2026. The report link and screenshot are provided below:
Conclusion
The research found that the viral video is not related to the alleged gang rape case in Sri Ganganagar, Rajasthan. The video is being shared with a false context. The footage actually belongs to a separate incident from Gujarat’s Patan district reported in March 2026.
