#FactCheck: Viral Avalanche Video Is from Peru, Not the Nepal-Tibet Floods
Executive Summary
A video showing an avalanche is going viral on social media, with users linking it to the devastating floods that struck Nepal-Tibet on August 26, 2026. Some users are sharing the video claiming that it is the first video of the Nepal floods and that it shows the same glacier whose collapse caused the devastating flooding in Nepal. However, a research by CyberPeace’s Research Wing found that the video is actually from Peru, where the incident took place in July 2026. The video has no connection to the floods that occurred in Nepal-Tibet.
Claim:
Facebook user ‘Adv. Shubh Karan Singh’ shared the video with the caption: “The most terrifying video of the Nepal deluge has emerged. This is the glacier that caused the tragedy in Nepal. The glacier fell into a river in Nepal from the Tibetan side, and this glacier is responsible for the flooding in Nepal.”
https://www.facebook.com/reel/1550707740405005
Fact Check
To investigate the viral claim, we conducted a reverse image search of keyframes from the video using Google Lens. We found that the video was posted on the YouTube channel of Peruvian news portal Diario Correo on July 28, 2026. The video was identified as being from Cusco. According to the information provided with the video, tourists in Cusco captured the avalanche at Humantay Lagoon on camera.
https://youtu.be/CM6ugW6v834?si=XEqWDZ2MqTtqrf3r
A report published by the Peruvian news website Perú 21 on July 27, 2026, used a keyframe from the viral video. According to the report, “A recent snowstorm near Humantay Lagoon, one of Cusco’s main tourist attractions, startled tourists visiting the area. Videos shared on social media show snow and chunks of ice falling down the mountain slopes, while tourists can be seen moving away from the area as a precaution. According to the information available so far, no injuries or missing persons have been reported following the fall of the ice mass.”
https://peru21.pe/peru/avalancha-en-laguna-humantay-sorprende-turistas-en-cusco/
According to information available on the UNESCO website, Cusco is located in the Peruvian Andes. The Peruvian Andes are a vast mountain range that stretches from north to south across Peru.
https://whc.unesco.org/en/list/273
Conclusion:
Our research found that the viral claim is false and misleading. The video being circulated as the “first video” of the devastating floods in Nepal and Tibet does not show a glacier collapse in the region or have any connection to the floods reported there on August 26, 2026. Through a reverse image search of keyframes, we traced the video to Cusco, Peru, where it was recorded in July 2026. Reports published by Peruvian media outlets identified the footage as showing an avalanche near Humantay Lagoon, a popular tourist destination in Cusco. The incident was captured on camera by tourists visiting the area, and there were no reports of injuries or missing persons at the time. Independent information from UNESCO also confirms that Cusco is located in the Peruvian Andes. Therefore, the viral video is an old video from Peru and is being falsely linked to the Nepal-Tibet floods. There is no evidence to support the claim that the footage shows the glacier collapse responsible for the flooding in Nepal.
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]
Executive Summary
A video is being widely shared on social media with the claim that protesters during the Cockroach Janta Party (CJP) protest in Delhi pelted stones at India’s first hydrogen train. The viral video shows a train parked at a railway station being attacked with stones amid chaos and heavy commotion. Social media users are claiming that the incident took place at a Delhi railway station, where protesters targeted the country’s new hydrogen train during the CJP march towards Jantar Mantar and damaged public property. CyberPeace Research Wing’s research found the claim to be false. The viral video is not related to the CJP protest or India’s first hydrogen train. The video is from Bihar, where candidates appearing for a Bihar Police Prohibition Department examination had protested over train delays, leading to clashes and stone-pelting.
Claim:
A social media post claimed:“New Delhi: During the march towards Jantar Mantar, a video of stone-pelting on a hydrogen train parked at a railway station is going viral. Some people can be seen throwing stones at the train. Damaging public property is a punishable offence. Further action will be taken based on official information from authorities.”
https://www.facebook.com/reel/1573292581055475
Fact Check:
To verify the authenticity of the viral claim, we extracted keyframes from the video and conducted a reverse image search using Google Lens. During the research, we found the same video clip uploaded on the Facebook page News Bihar on June 14, 2026. According to the post’s caption, the incident took place at Danapur–Patliputra railway station in Patna, Bihar, where thousands of candidates appearing for the Bihar Police Prohibition Department examination became agitated due to train delays. The protest escalated after candidates blocked railway tracks, leading to a confrontation between police personnel and students, including stone-pelting.
https://www.facebook.com/reel/2476273089511309
During further research, we found a video uploaded by Republic World’s YouTube channel on June 14, 2026, which also contained visuals matching the viral clip. The report stated that students in Bihar staged an aggressive protest after trains were delayed. During the protest, clashes broke out between police and students, and authorities used mild force to control the situation.
https://www.youtube.com/shorts/AlamDZzPKLA
During our research, we also found a reply from the official X account of Northern Railway on a similar viral post. In its response, Northern Railway clarified that the train visible in the viral video was not a hydrogen train. The railway authority stated that the video was old and confirmed that hydrogen train services are operating safely and as per their scheduled timetable.
Conclusion:
CyberPeace Research Wing’s research found that the claim linking the viral video to stone-pelting on India’s first hydrogen train during the CJP protest is false. The viral video is from Bihar, where candidates appearing for the Bihar Police Prohibition Department examination protested over train delays, resulting in clashes and stone-pelting at a railway station. The video has been falsely linked to the CJP protest and hydrogen train.
Executive Summary
A video is being shared on social media with the claim that a BJP MLA assaulted a man during a bridge inauguration ceremony in Assam. In the viral clip, a man is seen grabbing a young man and slapping him amid a crowd. He is then seen uprooting a banana plant and attacking the same man with it. A research done by the Research Wing of the CyberPeace found the claim accompanying the viral video to be misleading. The research revealed that the man seen assaulting the youth is not a BJP MLA, but former AIUDF MLA Samsul Huda. The video is being shared on social media with his identity falsely described as that of a BJP MLA.
Claim:
A Facebook user shared the viral video with the caption: “The arrogance of BJP leaders is at its peak, which means their end is now certain. He is a BJP MLA from Assam. When the inauguration ribbon turned pink instead of red, he lost his temper and started beating the person.” The post further stated: “If this mistake is considered a crime, then these MLAs should be beaten every day in the streets and neighbourhoods because they commit much bigger mistakes.”
The link to the post, its archive link, and a screenshot are provided below.
Factcheck
To verify the authenticity of the viral claim, the Desk first extracted keyframes from the video and conducted a reverse image search using Google Lens. During the search, the Desk found the same video on the official YouTube channel of Northeast Live, which was uploaded on March 18, 2025. The link to the post and a screenshot are provided below.
https://www.youtube.com/watch?v=L5zrgLbI6NA
According to the video report, former AIUDF MLA from Bilasipara East, Samsul Huda, lost his temper over an issue while attending the foundation stone-laying ceremony of a bridge in Bilasipara, Assam. During the incident, he assaulted an employee of the construction company who was standing nearby. As the research progressed, the Desk found a report published by India Today NE on March 20, 2025, which carried the same visuals from the viral video. The link to the report and a screenshot are provided below.
According to the report, former AIUDF MLA from Bilasipara East in Assam’s Dhubri district, Samsul Huda, had attended a bridge foundation stone-laying ceremony, where he assaulted an employee of the construction company.
Conclusion
The Desk’s research found the claim accompanying the viral video to be misleading. The person seen assaulting the youth in the video is not a BJP MLA, but former AIUDF MLA Samsul Huda. The video is being shared on social media with his identity falsely described as that of a BJP MLA.
