#FactCheck : Edited video falsely claims Dr. Vikas Divyakirti targeted Narendra Modi
Executive Summary
A video circulating on social media shows Dr. Vikas Divyakirti speaking during a podcast, where he is heard saying, “Those who cannot even memorise and speak four sentences are considered the greatest in India.” Several users are sharing the clip claiming that the remark was aimed at Narendra Modi. However, a research by CyberPeace found the claim to be misleading. The research revealed that the viral clip has been edited and shared out of context. In the original video, Divyakirti made the remarks in reference to film stars, not the Prime Minister.
Claim
On Facebook, a user shared the viral clip with an English caption alleging that Divyakirti criticised Modi, saying he cannot speak without a teleprompter or scripted interviews and has built a false image of greatness.
Similarly, another user shared the video on X, suggesting that people who cannot speak without a teleprompter are still considered great in India, indirectly linking the remark to Modi.
Fact Check
To verify the claim, we extracted keyframes from the viral video and conducted a reverse image search using Google Lens. This led us to the original video uploaded on the official YouTube channel of Raj Shamani.
At around the 3:55 mark, the same clip can be seen. During the conversation, Shamani asks whether building a larger-than-life perception actually benefits an individual. Responding to this, Dr. Vikas Divyakirti explains that film stars often have an exaggerated public image. He notes that many of the dialogues they are praised for are not written by them, but by others, and some even rely on teleprompters while speaking. He further adds that there are individuals who cannot even memorise and deliver four sentences or think independently, yet are regarded as great in India. He also mentions that many social media personalities use teleprompters, but audiences remain unaware and assume they possess exceptional knowledge.
Conclusion
The viral claim is misleading. The video has been edited and shared out of context. Dr. Vikas Divyakirti was referring to film stars and social media personalities, not Narendra Modi.
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Introduction
A digital forensic investigation can start with a question: what really happened? The tricky part is that the answer might be hidden in thousands of files, system logs, browser records, messages, application artefacts and timestamps. Traditional forensic practice gives a method to find, collect, check and report such evidence yet the amount of digital data keeps growing. NIST’s forensic guidance says that evidence must be kept safe its integrity checked and investigative steps written down so that results can be examined and repeated.[4] This is where Large Language Models (LLMs) draw interest. LLMs can. Summarise large amounts of text, spot connections between pieces of information and help an examiner move through evidence faster. However, an important question remains: can an LLM truly become an investigator or should it stay an assistant to one?
Understanding the basic idea: What is an LLM?
A Large Language Model is an AI system trained on collections of text so that it can learn language patterns and produce answers. In terms an LLM does not think like a human investigator. An LLM creates language from patterns it learned during training and from the details it receives. This makes an LLM handy for summarisation, classification, question answering and pulling information from text.
Where LLMs can actually help a forensic examiner
Finding relevant evidence faster
Consider a case involving a suspected phishing incident. A forensic examiner may have email headers, message bodies, attachments, browser history, DNS records and system logs. An LLM can help organize text-based evidence, spot repeated terms pull out indicators such as domains or IP addresses and cluster related events for review. Research on LLM-assisted forensics says that pattern recognition and early evidence analysis are promising use cases.[1][3]
Connecting the timeline
Investigations often rely on order: what happened first what followed and which artefacts back that order. An LLM can turn amounts of timestamped data into a clear timeline or point out records that seem related. The key point is that the LLM helps an examiner see relationships; it does not independently prove that one event caused another.
Making forensic reporting easier to understand
A good forensic report should be understandable to technical and non-technical readers. LLMs can help turn examiner notes or structured findings into clearer draft language, summaries or executive explanations. This is particularly useful when an investigation contains technical terms that need to be explained without losing their meaning. Research also identifies evidence presentation and reporting as a potential area for LLM assistance.[1]
A realistic example
Imagine an organisation reports that an employee account may have been compromised. The examiner collects the relevant disk image, authentication logs, browser history and email data using established procedures. After preservation and examination with validated tools, a controlled set of extracted text or structured artefacts could be given to an LLM. It might identify unusual login patterns, highlight a suspicious domain appearing in multiple sources, and draft questions for further examination.
The examiner then checks those observations against the original evidence and forensic tool outputs. If the model says a login occurred at 10:14, that timestamp must be verified in the source log. If it suggests that two events are linked, the relationship must be supported by evidence. The model can accelerate the search, but the evidence remains the foundation of the conclusion.
Why an LLM cannot simply replace the investigator
The biggest challenge is reliability. LLMs can produce fluent answers that sound convincing even when the answer is wrong. The 2026 systematic review of 33 peer-reviewed works on LLMs in digital forensics highlights hallucination, explainability, reproducibility and legal admissibility as major concerns.[1] In forensic work, an incorrect sentence is not just a minor inconvenience; it can change how a case is understood.
There is also a reproducibility problem. Traditional forensic practice depends on validated processes, integrity checks and documentation. NIST recommends verifying acquired data and recording actions and tools so work can be repeated.[4] LLM output can vary with the model, settings, context and system version. That makes raw model output unsuitable as forensic proof on its own.
Another concern is confidentiality. Evidence may contain personal information, credentials, private communications or sensitive organisational data. Sending it to an external AI service without proper controls can create a privacy and governance risk. NIST’s Generative AI Profile stresses managing risks across the AI lifecycle.[5]
What responsible LLM-assisted forensics could look like
A practical approach is to keep the examiner in control. The LLM should receive only the information needed for the task, preferably through a controlled environment with access restrictions and logging. Critical findings should always link back to the source artefact rather than being accepted because the model sounds confident.
A simple operational principle is: -
In practice, this means preserving and hashing evidence, using validated forensic tools for acquisition and examination, recording what data was supplied to the AI system, retaining relevant prompts and outputs in working notes, and independently verifying material claims. SWGDE guidance likewise emphasises protecting the integrity of evidence and documenting handling throughout the evidence lifecycle.[6]
A further possibility is a local or specialised forensic model. Research on “ForensicLLM” demonstrates this direction, including source attribution and retrieval-augmented approaches.[2] Such designs may provide a more controlled environment, but they still require testing and validation before operational use.
Conclusion
LLMs are unlikely to make the forensic examiner irrelevant. Their more realistic value is in helping the examiner deal with the scale and complexity of modern evidence. They can search, summarise, classify, correlate and help communicate findings, but these capabilities come with limitations that matter deeply in forensic work. A forensic conclusion must remain traceable to evidence, repeatable through a documented process and open to independent verification.[1][4]
The future of AI-assisted digital forensics is therefore less about replacing investigators and more about designing a disciplined partnership between human expertise and machine assistance. The strongest model is one in which AI speeds up the routine parts of an investigation while the examiner remains responsible for validation, interpretation and final conclusions. As research develops, the real question will not simply be whether an LLM is intelligent enough to analyse evidence, but whether the complete system around it is controlled, explainable and trustworthy enough for forensic use.
References / Endnotes
[1] Chernyshev, M., Baig, Z., Syed, N., Doss, R., & Shore, M. (2026). “Large language models in digital forensics: capabilities, challenges and future directions.” Forensic Science International: Digital Investigation, 56, 302043. https://doi.org/10.1016/j.fsidi.2025.302043
[2] “ForensicLLM: A local large language model for digital forensics.” Forensic Science International: Digital Investigation, 52, Supplement, 301872 (2025). https://doi.org/10.1016/j.fsidi.2025.301872
[3] Wickramasekara, A., Breitinger, F., & Scanlon, M. (2025). “Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency.” Forensic Science International: Digital Investigation, 52, 301859. https://doi.org/10.1016/j.fsidi.2024.301859
[4] Kent, K., Chevalier, S., Grance, T., & Dang, H. (2006). Guide to Integrating Forensic Techniques into Incident Response, NIST Special Publication 800-86. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-86
[5] Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1
[6] Scientific Working Group on Digital Evidence (SWGDE). Best Practices for Digital Evidence Collection, 18-F-002-2.0. https://www.swgde.org/documents/published-complete-listing/18-f-002-2-0/
Executive Summary
Our research confirms that the viral image showing Iranian soldiers standing near a crashed helicopter is AI-generated and has no connection to any real-world event. It is being misleadingly shared online amid geopolitical tensions. Amid rising tensions between Iran, the United States, and Israel, a dramatic image is being widely shared on social media. The picture shows soldiers standing near the wreckage of a crashed helicopter in a desert, holding an Iranian flag. Users claim that Iranian forces shot down the aircraft. Research by CyberPeace Research Wing found that the viral image is fake and was created using AI tools.
Claim
A Facebook page named “Official Salman 09” shared the image on May 1, 2026, portraying it as a powerful symbol of victory in an ongoing conflict. The post suggested that the image reflected Iran’s military success and carried a broader political message amid regional tensions.
- https://www.facebook.com/photo/?fbid=909905332099201&set=a.522993370790401
- https://perma.cc/KCL8-7UDN
Fact Check
To verify the claim, we first conducted a reverse image search using Google Lens. The image did not appear on any credible news platforms, although it was widely circulating across social media—raising suspicion about its authenticity. We then analyzed the image using Google’s SynthID detector, which confirmed with high confidence that the image was generated using Google’s AI tools. SynthID is a technology designed to watermark and identify AI-generated content.
Further verification using AI detection tool Hive Moderation indicated a very high likelihood (up to 99.9%) that the image was AI-generated, with strong probability that it was created using Google’s Gemini.
Conclusion
Our research confirms that the viral image showing Iranian soldiers standing near a crashed helicopter is AI-generated and has no connection to any real-world event. It is being misleadingly shared online amid geopolitical tensions.
Executive Summary
A video showing thick smoke rising from a building and people running in panic is being shared on social media. The video is being circulated with the claim that it shows Iran launching a missile attack on the United States.CyberPeace’s research found the claim to be misleading. Our probe revealed that the video is not related to any recent incident. The viral clip is actually from the September 11, 2001 terrorist attacks on the World Trade Center in the United States and is being falsely shared as footage of an alleged Iranian missile strike on the US.
Claim:
An Instagram user shared the video claiming, “Iran has attacked a US airbase in Qatar. Iran has fired six ballistic missiles at the Al Udeid Airbase in Qatar. Al Udeid Airbase is the largest US military base in West Asia.”
Links to the post and its archived version are provided below.
Fact Check:
To verify the claim, we extracted key frames from the viral video and ran a reverse image search using Google Lens. During the search, we found visuals matching the viral clip in a report published by Wion on September 11, 2021. The report, titled “In pics | A look back at the scenes from the 9/11 attacks,” included an image that closely resembled the visuals seen in the viral video. The caption of the image stated that it was a file photo from September 11, 2001, showing pedestrians running as one of the World Trade Center towers collapsed in New York City.
Further research led us to the same footage on the YouTube channel CBS 8 San Diego. At the 01:11 timestamp of the video, visuals matching the viral clip can be clearly seen.
We also found an Al Jazeera report dated June 23, 2025, which confirmed that Iran had attacked US forces stationed at the Al Udeid airbase in Qatar in retaliation for US strikes on Iran’s uclear facilities. However, the visuals used in the viral video do not correspond to this incident.
Conclusion
The viral video does not show a recent Iranian attack on a US airbase in Qatar. The clip actually dates back to the September 11, 2001 terrorist attacks on the World Trade Center in the United States. Old 9/11 footage has been falsely shared with a misleading claim linking it to Iran’s alleged missile strike on the US.
