#FactCheck-AI-Generated Image of Virat Kohli and Anushka Sharma Falsely Shared as a Real Candid Moment
Executive Summary
A picture is rapidly going viral on social media, showing Indian cricketer Virat Kohli and actor Anushka Sharma having breakfast together. Users are sharing this photo, presenting it as a "candid" (real) moment. Research by the CyberPeace Research Wing revealed that the photo of Virat Kohli and Anushka Sharma having breakfast is completely fake. This image does not depict a real moment, but has been created using Artificial Intelligence (AI).
Claim
A picture is rapidly going viral on social media, showing Indian cricketer Virat Kohli and actor Anushka Sharma having breakfast together. Users are sharing this photo, presenting it as a "candid" (real) moment.
https://www.facebook.com/groups/1132434027856845/posts/1677540760012833/
Fact Check
In our research, this image was found to be 'AI-generated'. When a reverse image search and keyword scan were conducted to verify this viral photo, no credible media reports, official photographs, or any such posts on the celebrity couple's official social media handles were found.
WASIT also confirmed that the image is 'AI-generated'.
Conclusion
Our research revealed that the photo of Virat Kohli and Anushka Sharma having breakfast is completely fake. This image does not depict a real moment, but has been created using Artificial Intelligence (AI).
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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
A video showing a large crowd disrespecting the Indian tricolor along with American and Israeli flags is being widely shared on social media. Users sharing the video claim that the incident recently took place in Iran's capital, Tehran, showcasing "Iran's hatred towards India." However, a fact-check done by the CyberPeace Research Wing has revealed that this claim is misleading. The viral video was not filmed in Iran, but rather during a procession in Karachi, Pakistan.
The Claim
A user on the social media platform X (formerly Twitter) shared the video with the caption: "India’s biggest enemy is burning our flag and guess who is sitting silently? Iran is not our friend. Their hatred towards India is clearly visible, but our tricolor is being burnt in Iran." https://x.com/bhagwakrantivir/status/2071096021183246667 , https://archive.ph/6mGE8
Factcheck
Keyframes extracted from the viral video were searched using Google Lens. This led to an Instagram post containing the exact same footage. The caption of this post identified the location as Karachi, Pakistan, noting that the demonstration was held in support of Iran, during which flags of India, the US, and Israel were burnt.
https://www.instagram.com/reels/DaDLQO6obcK/
Further keyword searches led to the official Instagram account of the 'Imamia Students Organization Karachi'. A video uploaded on June 25, 2026, showed the same event captured from a different camera angle. According to the caption, the footage is from a '9th Muharram' procession in Karachi.
https://www.instagram.com/p/DaApSBfIpNQ/
To conclusively establish the location, Google Maps Street View was used to cross-reference the physical landmarks seen in the video.The visual elements perfectly match Mohammad Ali Jinnah Road in Karachi, Pakistan.A distinct blue building named 'Mid Town', visible in the viral video, matches the Google Street View imagery of the location.
Conclusion
The research confirms that the video showing the burning and desecration of the Indian national flag is from Karachi, Pakistan, and not Tehran, Iran. The social media claims linking this incident to Iran are false and contextually misleading.
Introduction
In today's era of digitalised community and connections, social media has become an integral part of our lives. we use social media to connect with our friends and family, and social media is also used for business purposes. Social media offers us numerous opportunities and ease to connect and communicate with larger communities. While it also poses some challenges, while we use social media, we come across issues such as inappropriate content, online harassment, online stalking, account hacking, misuse of personal information or data, privacy issues, fake accounts, Intellectual property violation issues, abusive and dishearted content, content against the terms and condition policy of the platform and more. To deal with such issues, social media entities have proper reporting mechanisms and set terms and conditions guidelines to effectively prevent such issues and by addressing them in the best possible way by platform help centre or reporting mechanism.
The Role of Help Centers in Resolving User Complaints:
The help centres are established on platforms to address user complaints and provide satisfactory assistance or resolution. Addressing user complaints is a key component of maintaining a safe and secure digital environment for users. Platform-centric help centres play a vital role in providing users with a resource to seek assistance and report their issues.
Some common issues reported on social media:
- Reporting abusive content: Users can report content that they find abusive, offensive, or in violation of platform policies. These reports are reviewed by the help centre.
- Reporting CSAM (Child Sexual Abuse Material): CSAM content can be reported to platform help centre. Social media platforms have stringent policies in place to address such concerns and ensure a safe digital environment for everyone, including children.
- Reporting Misinformation or Fake News: With the proliferation of misinformation online, users can report content that they find or suspect misleading or false information and Fact-checking bodies are employed to assess the accuracy of reported content.
- Content violating intellectual property rights: If there is a violation or infringement of any intellectual property work, it can be reported on the platform.
- Violence of commercial policies: Products listed on social media platforms are also needed to comply with the platform’s Commercial Policies.
Submitting a Complaint to the Indian Grievance Officer for Facebook:
A user can report his issue through the below-mentioned websites:
The user can go to the Facebook Help Center, where go to the "Reporting a Problem” section, then by clicking on Reporting a Problem, Choose the Appropriate Issue that best describes your complaint. For example, if you have encountered inappropriate or abusive content, select the ‘I found inappropriate or abusive content’ option.
Here is a list of issues which you can report on Facebook:
- My account has been hacked.
- I've lost access to a page or a group I used to manage.
- I've found a fake profile or a profile that's pretending to be me.
- I am being bullied or harassed.
- I found inappropriate or abusive content.
- I want to report content showing me in nudity/partial nudity or in a sexual act.
- I (or someone I am legally responsible for) appear in content that I do not want to be displayed.
- I am a law enforcement official seeking to access user data.
- I am a government official or a court officer seeking to submit an order, notice or direction.
- I want to download my personal data or report an issue with how Facebook is processing my data.
- I want to report an Intellectual Property infringement.
- I want to report another issue.
Then, describe your issues and attach supporting evidence such as screenshots, then submit your report. After submitting a report, you will receive a confirmation that your report has been submitted to the platform. The platform will review the complaint within the stipulated time period, and users can also check the status of their filed complaint. Appropriate action will be taken by platforms after reviewing such complaints. If it violates any standard policy, terms & conditions, or privacy policies of the platform, the platform will take down that content or will take any other appropriate action.
Conclusion:
It is important to be aware of your rights in a digital landscape and report such issues to the platform. It is essential to understand how to report your issues or grievances on social media platforms effectively. By using the help centre or reporting mechanism of the platform, users can effectively file their complaints on the platform and contribute to a safer and more responsible online environment. Social media platforms have their compliance framework and privacy and policy guidelines in place to ensure the compliance framework for community standards and legal requirements. So, whenever you encounter an issue on social media, report it on the platform and contribute to a safer digital environment on social media platforms.
References:
- https://www.cyberyodha.org/2023/09/how-to-submit-complaint-to-indian.html
- https://transparency.fb.com/en-gb/enforcement/taking-action/complaints-handling-process/
- https://www.facebook.com/help/contact/278770247037228
- https://www.facebook.com/help/263149623790594
