#FactCheck-AI-Generated Video Falsely Shared as Drone Footage of Flooded Surat, Gujarat
Executive Summary
A 20-second video showing skyscrapers submerged in water is being widely circulated on social media with the claim that it depicts the flood situation in Surat, Gujarat. The video is also being used by some users to criticize the Bharatiya Janata Party (BJP) government in Gujarat and Prime Minister Narendra Modi. Many social media users are sharing the clip as authentic footage of flooding in Surat. CyberPeace Research Wing’s research found the viral claim to be false. Our research revealed that the video is AI-generated and has no connection with Surat. While it is true that heavy rainfall caused flooding in parts of Surat and nearby districts in early July 2026, the viral video does not show any real scenes from the city.
Claim
Facebook user ‘Amresh Kumar’ shared the viral video on July 16, 2026, with the caption, “Thirty years of the Modi-BJP government. Drone footage of Gujarat Model – Surat city.”
https://www.facebook.com/reel/2440459239767277
Fact Check
To verify the claim, we first conducted a keyword search using terms related to flooding in Surat during July 2026. Our search confirmed that Surat experienced heavy rainfall during the first week of July, resulting in waterlogging and disruption in several low-lying areas. According to a report published by Bhaskar.com on July 8, 2026, intense rainfall led to flooding in several parts of Surat, and a few deaths due to electrocution were also reported.
However, none of the credible reports or images from the actual flooding resembled the scenes shown in the viral video. To further investigate, we analysed the video using multiple AI detection tools.We first scanned the video using Hive Moderation, which indicated a 99.9% probability that the footage was generated using Artificial Intelligence.
For additional verification, we examined the clip using another AI detection platform, WasItAI, which also concluded that the video had been created using AI. The analysis from multiple tools strongly suggests that the viral footage is digitally generated and does not represent a real event.
Conclusion
Our research found that the viral video being shared as drone footage of flooding in Surat is fake. Although Surat experienced heavy rainfall and flooding in early July 2026, the video circulating on social media is AI-generated and has no connection with the actual situation in the city. The clip is being falsely shared to spread misleading information.
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Introduction
Meta has announced that E2EE in Instagram direct messages is ending entirely. Every day, billions of people send messages they consider private. A medical update to a family member. A photograph meant for one person. A conversation they would never have in public. For years, end-to-end encryption (E2EE) was the technology that made that privacy possible: the digital equivalent of a sealed envelope that only the sender and receiver could open. After May 8, 2026, this will change.
Understanding the Adoption Gap
Meta pointed to low user adoption as the reason for this change. Few people were using encrypted messaging on Instagram, the company said, so the feature was not worth keeping. That explanation raises some questions. Encryption was never switched on by default. Users had to find it and turn it on themselves. It was not advertised. And it was only available in certain regions to begin with, something Meta noted on its own Help Centre page. Features that require users to actively seek them out tend to get used far less than those that simply work from the start. WhatsApp demonstrates this clearly; encryption has been on by default since 2016, for every user, with no action required. Back in 2019, Mark Zuckerberg spoke publicly about building privacy into Meta’s messaging platforms as a core direction for the company. The current decision shows a different vision for the company.
The Commercial Dimension
Encrypted message content is not accessible for advertising purposes by design. In December 2025, Meta updated its privacy policy to allow interactions with its Meta AI assistant to inform personalized advertising recommendations across its platforms. With encryption removed from Instagram direct messages, the content of those conversations enters a data environment that already serves Meta’s advertising systems. Meta has not made a direct public statement connecting these two decisions, but technology analysts and privacy researchers have noted the commercial implications of making previously inaccessible message content available within that ecosystem.
What This Means for Users
From May 8, 2026, the content of Instagram direct messages will be accessible to Meta’s systems. This includes messages relating to personal matters that users may have previously sent under the assumption of encryption. A related concern is the question of data security. Unencrypted message content stored on platform servers creates a larger surface area of sensitive information that could be exposed in the event of a security breach. As platforms collect and retain greater volumes of personal data, the potential consequences of unauthorised access grow correspondingly.
But, there is an argument on the other side. Law enforcement agencies and child safety organisations have long maintained that end-to-end encryption limits their ability to detect and act on harmful content. Removing encryption does make certain forms of platform-level content moderation technically feasible where they were not before.
India’s Supreme Court: The Warning Nobody Heeded
India’s Supreme Court said it plainly when hearing the case against Meta’s 2021 WhatsApp privacy policy, which forced hundreds of millions of users to accept data sharing with Facebook or lose access entirely. Chief Justice Surya Kant called it “a decent way of committing theft of private information” and asked how ordinary people could meaningfully consent to policies written in language they cannot understand. He made it human with one line: “A poor woman selling fruits on the streets — will she understand the terms of your policy?” The court ordered Meta not to share a single word of user data until the case is resolved. When Meta’s lawyers argued that encryption protects users anyway, the bench pushed back: encryption protects message content, not the metadata surrounding it. Who you talk to, how often, at what time, from where: all of it is still harvested. The Competition Commission’s own advocate summarised the entire arrangement in four words: “We are the products.”
WhatsApp: A Question Worth Asking
Instagram, Messenger, and WhatsApp are three products inside one ecosystem, owned by Meta, serving one business model. Instagram’s encryption is already gone. Is WhatsApp next in line ?
WhatsApp has over 850 million monthly active users in India alone. People do not use it for entertainment, it is how families talk, how businesses run, how essential daily communication happens. It is infrastructure, not an app. Meta acquired it in 2014 promising no ads, no data exploitation. By 2021 that promise was already bending. By 2025 ads appeared in the Status section. Both original co-founders had long since left the company over exactly these concerns. Instagram’s encryption survived until it conflicted with revenue and regulation. WhatsApp’s encryption exists today under the same ownership, the same business model, and the same tightening global regulatory pressure. That is not a reason to panic. It is a reason to pay attention.
Conclusion
Encryption is not permanent. It is a design choice, and like any design choice, it can be undone when priorities shift. After May 8, 2026, Instagram direct messages will no longer be protected the way they once were. For most users, this change will pass unnoticed. But the data those conversations contain will now be accessible in ways it previously was not. What platforms do with user data is rarely announced loudly. Paying attention to the quiet changes matters.
References
- https://help.instagram.com/491565145294150
- https://www.theguardian.com/technology/2026/mar/18/instagram-to-remove-end-to-end-encryption-for-private-messages-in-may
- https://www.androidpolice.com/why-meta-is-getting-rid-of-e2ee/
- https://digitalpolicyalert.org/change/13307
- https://www.skadden.com/insights/publications/2025/06/take-it-down-act
- https://timesofindia.indiatimes.com/india/you-cant-play-with-right-of-privacy-of-citizens-scs-big-warning-to-whatsapp-meta-over-take-it-or-leave-it-policy/articleshow/127878524.cms#
- https://proton.me/blog/instagram-end-to-end-encryption
- https://www.forbes.com/sites/parmyolson/2018/09/26/exclusive-whatsapp-cofounder-brian-acton-gives-the-inside-story-on-deletefacebook-and-why-he-left-850-million-behind/
Introduction
You ask an app for directions to a street you've driven down a hundred times. You let autocomplete finish your sentence before you've decided what you meant to say. You take a photo of a document instead of reading it, trusting the summary a model hands back. None of these moments feel like a loss. Each one is, on its own, a reasonable trade of effort for convenience. But add them up across a year, a career, an industry, and you start to wonder what exactly we've been trading away. Every technology wave produces its own founding myth. For AI, the myth is that intelligence can be manufactured at scale, bottled into a model, and dispensed on demand - cheaper, faster, and eventually better than the human original. It's a seductive story, and one we've been telling ourselves so uncritically that we've stopped noticing what it costs.
The casualties of this bet are rarely dramatic. Nobody announces that a skill has quietly atrophied, or that a habit of independent judgement has gone unused long enough to weaken. These losses don't show up as headlines; they show up later, as gaps, when the system that was supposed to think for us turns out not to have been thinking at all. Ford Motor Company's recent decision to rehire around 350 veteran engineers, after leaning heavily on AI-driven quality systems, is a small but telling data point.¹ The lesson isn't that automation failed outright — it's that a process can be automated without the judgement that made the process work ever being captured in the first place. That distinction between automating a task and actually preserving the human expertise behind it is the real subject of this AI moment.
How Organisations Are Using AI in Decision-Making
More organisations are now leaning on AI not just to execute tasks, but to help shape decisions. Deloitte's 2026 Global Human Capital Trends survey found that 60% of executives now regularly use AI to support their decisions, and the same report cites Gartner's projection that by 2027, half of all business decisions will be augmented or automated by AI agents. Companies like Netflix and Amazon are often pointed to as examples of this working well using AI to enhance recommendations and logistics while keeping people involved in the interpretation, generating significant value in the process. Elsewhere, results have been more mixed: MIT's "State of AI in Business 2025" study found that 95% of generative AI pilots showed no measurable P&L impact within six months, often because this initiative failed to integrate feedback or adapt to context rather than because the underlying model was flawed. Critics have noted the study used a narrow definition of success (six-month, bottom-line ROI), so the figure may understate the value AI creates in ways that aren't captured on the P&L. Notably, this is not an argument against using AI. It is an argument about how we use it and why the human-in-the-loop principle, keeping people actively involved in judgement rather than passively rubber-stamping outputs, is not a compliance checkbox but the thing that determines whether automation actually works. That distinction, between automating a task and preserving the human expertise behind it, is the point of contention.
Finding the Balance
The lesson isn't to use AI less, it's to be deliberate about where it sits in the process. The strongest results come from pairing AI's speed with human judgement, not swapping one for the other. That means keeping a clear owner for important decisions, checking that the model is optimising for the right goal, and treating its output as a strong first draft rather than a final answer. Used this way, AI doesn't replace thinking, it gives good judgement more room to work.
Two Framings We Should Retire
Conversations about AI adoption keep falling into two lazy framings. The first is AI versus humans, as if technology and workforce are locked in a zero-sum contest for relevance. The second is AI versus jobs, reducing every discussion to headcount and displacement. Both are legitimate concerns, but they crowd out a more urgent question: as AI gets embedded deeper into how decisions are made, what happens to the quality of the decisions themselves? This is not a question about whether AI is useful and it plainly is. It is a question about what gets quietly outsourced along with the task, and whether anyone notices before it matters.
Why “Wisdom of Crowds” Does Not Automatically Apply to AI
A comforting analogy often gets reached for: surely, with millions of people using the same models, errors will average out, the way independent forecasters tend to converge on accurate estimates.² That analogy breaks down where it matters most. The wisdom-of-crowds effect depends on independent thinking, genuinely diverse information, and an aggregation mechanism that does not distort the signal. When millions of people query the same underlying model, those conditions collapse. Everyone draws from the same statistical engine, trained on overlapping data, tuned toward similar “safe” answers. The apparent agreement is not corroboration, it is an echo. This creates a genuinely new risk: AI can be confidently, fluently, and uniformly wrong across an entire organisation at once, without the friction that would normally surface an error in a single person's judgement.
The Casualties, Named Plainly
Several things erode quietly when organisations are not deliberate about integrating AI into decisions. Independent judgement is the first casualty of the willingness to form a view before checking what the model says. Verification effort follows: generative AI collapses retrieval and generation into one fluent output, and people invest less effort checking something that already sounds complete and well-reasoned. Diversity of thought narrows as more decision-makers lean on the same handful of models for research and drafting, quietly reducing the range of framings available when it matters most. Accountability becomes harder to trace when a recommendation generated by a model and passed along with minimal scrutiny creates a strange vacuum where a decision was made but nobody quite owns it. And informational anchoring sets in, where a signal becomes a coordination point simply because everyone is looking at it, regardless of its accuracy.
Why Human-in-the-Loop Is a Design Requirement
“Human in the loop” often becomes a rubber-stamp step rather than genuine scrutiny. That is a mistake, because the functions humans provide are structural, not decorative. Context that a model cannot infer history, relationships, unstated constraints shapes whether a reasonable-sounding answer is right in a specific situation. Domain expertise built over years lets someone recognise when a fluent answer is subtly wrong. Ethical judgement decides trade-offs a model has no standing to make on an organisation's behalf. Accountability means someone can be asked why a decision was made and answer from reasoning, not from “the system recommended it.” And the rarest function of all is the willingness to challenge a convincing answer and resisting the very fluency that makes AI output persuasive.
What This Looks Like in Practice
For organisations, the goal is not slowing AI adoption but being deliberate about where human judgement stays load-bearing. AI output should default to draft status until a qualified person has actively tested its logic against context the model lacks. Teams using the same AI tools for analysis should build in a step that actively seeks disagreement, rather than assuming convergence means correctness. Ford's decision to bring engineers back to lead design reviews is instructive: expertise, once encoded into a system, is not safe to let atrophy in the people who built it.³ Verification should be visible and required for decisions with real financial, legal, safety, or reputational consequences. And organisations should track which decisions were AI-assisted and who owned the final call, so accountability stays traceable rather than quietly disappearing.
Conclusion
Decades ago, management thinkers warned that automating a broken process only helps an organisation fail faster. The AI era raises the stakes on that warning: judgement itself, the hard-won capacity to reason well under uncertainty, can be automated away without anyone deciding to give it up. Machines already process information faster than any team of people. What they cannot yet do is originate the wisdom that comes from human experience, accountability, and the willingness to be told one is wrong. That capacity erodes not because AI is powerful, but because organisations stop deliberately exercising it. The real task ahead is not resisting AI, but ensuring that as it takes on more of the work of deciding, humans deliberately keep hold of the responsibility of deciding.
References
- https://www.assemblymag.com/articles/100186-ford-rehires-veteran-engineers-to-improve-ai-vehicle-quality
- https://finance.yahoo.com/technology/ai/articles/ford-rehires-veteran-engineers-ai-144332497.html
- https://finance.yahoo.com/technology/ai/articles/ford-rehires-more-300-engineers-162210705.html
- https://www.msn.com/en-us/money/other/ford-rehires-hundreds-of-engineers-after-ai-struggles-to-improve-quality/ar-AA26P4QB?ocid=BingNewsSerp
- https://www.foxbusiness.com/technology/ford-rehires-experienced-engineers-after-ai-misses-mark
- https://www.livemint.com/opinion/online-views/artificial-wisdom-of-crowds-jobs-crisis-ai-technology-automation-openai-model-11785009289768.html
- https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends/2026/decision-making-with-ai.html
- https://www.hpcwire.com/aiwire/2026/03/04/deloittes-state-of-ai-2026-why-enterprise-execution-is-falling-behind-adoption/ and Legal.io summary: https://www.legal.io/blog/5719519/MIT-Report-Finds-95-of-AI-Pilots-Fail-to-Deliver-ROI-Exposing-GenAI-Divide
- https://www.marketingaiinstitute.com/blog/mit-study-ai-pilots
Executive Summary
Following a recent protest at Delhi’s Jantar Mantar, members of the self-styled “Cockroach Janata Party” also staged a demonstration in Pune, Maharashtra, demanding the resignation of Union Education Minister Dharmendra Pradhan. Amid this backdrop, a photograph has gone viral on social media purportedly showing the party’s founder, Abhijeet Dipke, posing with Prime Minister Narendra Modi. CyberPeace Research Wing research found that the image is not genuine. The photograph has been created using Artificial Intelligence (AI) and does not depict a real meeting between the two individuals.
Claim
An X user, “subhash Kumar sharma,” shared the viral image on June 8, 2026, with the caption: “This is the real face of the Cockroach... the rest you all understand. They were begging for Pradhan’s resignation at Jantar Mantar… did they get it or not?” The archived and original links to the post can be seen below:
https://x.com/sharmass27/status/2063691212662686102?s=20
Fact Check
To verify the claim, we conducted a reverse image search using Google. However, we did not find any credible news reports related to the viral photograph. We also searched using relevant keywords, but no reliable information supporting the claim was found. We then examined the official X account of Abhijeet Dipke, founder of the “Cockroach Janata Party.” No post, photograph, or information related to any meeting with Prime Minister Narendra Modi was found on his account.
https://x.com/abhijeet_dipke?lang=en
The absence of any credible evidence led us to suspect that the image may have been generated using AI. To further verify its authenticity, we analyzed the image using AI-detection tools. The first tool, Hive Moderation, indicated a nearly 99 percent probability that the image was AI-generated. The analysis also suggested signs consistent with content produced by generative AI models.
We then examined the image using another AI-detection platform, Sightengine. Its analysis likewise indicated a 99 percent likelihood that the image was AI-generated and showed characteristics commonly associated with AI-created visuals.
Conclusion
Our research found that the claim accompanying the viral image is false. The photograph purportedly showing Prime Minister Narendra Modi with Cockroach Janata Party founder Abhijeet Dipke is not real. The image has been generated using Artificial Intelligence and does not represent an actual meeting or event.
