#FactCheck - Viral Images of Indian Army Eating Near Border area Revealed as AI-Generated Fabrication
Executive Summary:
The viral social media posts circulating several photos of Indian Army soldiers eating their lunch in the extremely hot weather near the border area in Barmer/ Jaisalmer, Rajasthan, have been detected as AI generated and proven to be false. The images contain various faults such as missing shadows, distorted hand positioning and misrepresentation of the Indian flag and soldiers body features. The various AI generated tools were also used to validate the same. Before sharing any pictures in social media, it is necessary to validate the originality to avoid misinformation.
Claims:
The photographs of Indian Army soldiers having their lunch in extreme high temperatures at the border area near to the district of Barmer/Jaisalmer, Rajasthan have been circulated through social media.
Fact Check:
Upon the study of the given images, it can be observed that the images have a lot of similar anomalies that are usually found in any AI generated image. The abnormalities are lack of accuracy in the body features of the soldiers, the national flag with the wrong combination of colors, the unusual size of spoon, and the absence of Army soldiers’ shadows.
Additionally it is noticed that the flag on Indian soldiers’ shoulder appears wrong and it is not the traditional tricolor pattern. Another anomaly, soldiers with three arms, strengtheness the idea of the AI generated image.
Furthermore, we used the HIVE AI image detection tool and it was found that each photo was generated using an Artificial Intelligence algorithm.
We also checked with another AI Image detection tool named Isitai, it was also found to be AI-generated.
After thorough analysis, it was found that the claim made in each of the viral posts is misleading and fake, the recent viral images of Indian Army soldiers eating food on the border in the extremely hot afternoon of Badmer were generated using the AI Image creation tool.
Conclusion:
In conclusion, the analysis of the viral photographs claiming to show Indian army soldiers having their lunch in scorching heat in Barmer, Rajasthan reveals many anomalies consistent with AI-generated images. The absence of shadows, distorted hand placement, irregular showing of the Indian flag, and the presence of an extra arm on a soldier, all point to the fact that the images are artificially created. Therefore, the claim that this image captures real-life events is debunked, emphasizing the importance of analyzing and fact-checking before sharing in the era of common widespread digital misinformation.
- Claim: The photo shows Indian army soldiers having their lunch in extreme heat near the border area in Barmer/Jaisalmer, Rajasthan.
- Claimed on: X (formerly known as Twitter), Instagram, Facebook
- Fact Check: Fake & Misleading
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Introduction
In real-time warfare scenarios of this modern age, where actions occur without delay, the relevance of edge computing emerges as paramount. By processing data close to the source in the battlefield with the help of a drone or through video imaging from any military vehicle or aircraft, the concept of edge computing allows the military to point targets faster and strike with accuracy. It also enables local processing to relay central data, helping ground troops get intelligence inputs to act rapidly in critical mission scenarios.
As the global security landscape experiences a significant transformation in different corners of the world, it presents unprecedented challenges in the present scenario. In this article, we will try to understand how countries can maintain their military capabilities with the help of advanced technologies like edge computing.
Edge Computing in Modern Warfare
Edge computing involves the processing and storage of data at the point of collection on the battlefield, for example, through vehicles and drones, instead of relying on centralized data centers. This enables faster decision-making in real-time. This approach creates a resilient and secure network by reducing reliance on potentially compromised external connections, supporting autonomous systems, precision-based targeting, and data sharing among military personnel, drones, and command centers amidst a challenging environment.
A report released by the US Department of Defence in March 2025 found a crucial reality surrounding the operation of hardware relying on outdated industrial-age processes in the digital era. In the case of applications with video, edge computing helps to deliver significant advantages to a wide range of crucial military operations, which include:
- Situational awareness with real-time data processing that provides improved battlefield visibility and proper threat detection.
- Autonomous warfare systems such as drones, which use a tactical edge cloud computing to get the capability to navigate faster.
- Developing a strong communication and networking capability to secure low-latency communication for troops to stay connected in challenging environments.
- Ensuring predictive maintenance with the help of effective sensors to carry out edge detection and attrition at an early point, thereby reducing equipment failures.
- Developing effective targeting and weapons systems to ensure faster processing to enable precision-based targeting and response, besides a strong logistics and supply chain that can provide real-time tracking to improve delivery accuracy and resource management.
This report also highlighted that the DoD is rapidly updating its software and investing in AI enablers like data sets or MLOps tools. This also stresses the breaking down of integration barriers by enforcing MOSA (Modular Open Systems Approaches), APIs (Application Programming Interface), and modular interfaces to ensure interoperability across platforms, sensors, and networks to make software-defined warfare an effective strategy.
Developing Edge with Artificial Intelligence for Future Warfare
A significant insight from the work of the US Department of Defense is its emphasis on the importance of edge computing in shaping the future of warfare. In that context, the Annual Threat Assessment Report highlights a key limitation of traditional AI strategies that rely on centralised cloud computing, since these might not be suitable for modern battlefields with congested networks and limited bandwidth. The need for real-time data processing requires a distributed and edge-based AI solution to address contemporary threats. This report also directly supports the deployment of effective edge with AI in a defined, disrupted, intermittent, and limited-bandwidth (DDIL) environment. In that case, when the communication networks fail, the edge servers at the edge of the network offer crucial advantages that cloud-dependent systems cannot. This ability to analyse data and make decisions without consistent connectivity and operate with limited computational resources is a strategic necessity.
The scenario of warfare is a phenomenon that requires maintaining a strong strategic and tactical approach, which, in the present times, is being examined through the domain of digital platforms. Modern warfare patterns demand faster decision-making and edge computing deliveries by shifting the power of distant servers to the frontlines. The US military is already moving in the direction of deploying edge-enabled systems to prove the nature of sensors and networks to compute at the tactical edge to transform warfighting.
However, it can be understood with the help of an example, as creating fusion in the skies with F-35s. As they have showcased the capability of edge computing by fusing sensor data with MADL (Multi-Functional Advanced Data Link) to create a unified picture, making the squadrons a force multiplier. An example of this was visible when an F-35 relayed real-time tracking data, enabling a navy ship to neutralise a missile beyond its range.
Conclusion: The Way Ahead
As the changing nature of warfare moves towards adopting software-defined systems, where edge computing thrives as a futuristic military technology, it calls for the need for integration across all domains of warfighting. But at the same time, several imperatives do emerge, such as:
- Developing an open architecture that enables both flexibility and innovation.
- Ensuring an effective connectivity that actually combines a confluence of legacy systems.
- Developing interoperability among the systems that can function in synergy with all platforms and can function across all domains.
- Prioritising edge-native AI development systems, where it is also necessary to ensure the shift to adopting cloud-based AI models to create solutions optimised from the ground up for edge deployment.
- Investing in edge infrastructure to establish a robust edge computing infrastructure that enables rapid deployment by testing and updating AI capabilities across diverse hardware platforms. Like the way the military training academies in India are developing training infrastructures for training officer cadets or personnel to handle drones and all forms of advanced warfare tactics emerging in this age.
- Fostering talent and expertise by embracing commercial solutions where software talent could be enabled across the enterprises with expertise in edge computing capabilities and AI. In this case, the role of the commercial sector can help to drive innovations in edge AI, and the only way to move in this direction is by leveraging these advances through partnerships and collaborative efforts.
Taking the example of the ARPANET, which once seeded the modern internet, edge computing can also help to create a transformative network effect within the digital battlespace. In conclusion, future conflicts will be defined by the speed and accuracy provided by the edge, as nations integrating AI and robust edge infrastructures can hold a strong advantage in the multi-domain battlefields in the future.
References
- https://www.idsa.in/mpidsanews/rk-narangs-article-what-the-regions-first-drone-warfare-taught-us-published-in-the-new-indian-express
- https://latentai.com/blog/software-defined-warfare-why-edge-ai-is-critical-to-americas-defense-future/
- https://www.boozallen.com/s/insight/blog/how-the-us-military-is-using-edge-computing.html
- https://capsindia.org/wp-content/uploads/2022/08/RK-Narang-3.pdf
- https://www.newindianexpress.com/opinions/2025/May/12/what-the-regions-first-drone-warfare-taught-us
- https://www.maris-tech.com/blog/edge-computing-in-the-military-challenges-and-solutions/#:~:text=In%20modern%20warfare%2C%20decisions%20need,enables%20precision%20targeting%20and%20response
- https://cassindia.com/digital-soldiers/
Executive Summary
A video circulating on social media shows an electric car allegedly being powered by a portable generator attached to it. The clip is being shared with the claim that the generator is directly running the vehicle, suggesting a groundbreaking or unusual technological feat. However, research conducted by the CyberPeace found the viral claim to be false. Our research revealed that the video is not authentic but AI-generated.
Claim
On February 22, 2026, a user on X (formerly Twitter) shared the viral video with the caption: “After watching this video, Newton might turn in his grave.” The post implied that the video demonstrates a scientific impossibility.
Fact Check:
To verify the claim, we conducted a keyword search on Google. However, we found no credible reports from any reputable media organization supporting the assertion made in the viral post. A close examination of the video revealed several visual inconsistencies and unnatural elements, raising suspicion that the footage may have been generated using artificial intelligence. We then analyzed the video using the AI detection tool Hive Moderation. The results indicated a 96 percent probability that the video was AI-generated.
In the next step of our research , we scanned the video using another AI detection platform, WasItAI, which also concluded that the viral video was AI-generated.
Conclusion
Our research confirms that the viral video is not real. It has been artificially created using AI technology and is being circulated with a misleading claim.
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
