#FactCheck - Viral Image of Broken Mahatma Gandhi Statue Is Not from Bangladesh
An image showing a damaged statue of Mahatma Gandhi, broken into two pieces, is being widely shared on social media. The image shows Gandhi’s statue with its head separated from the body, prompting strong reactions online.
Social media users are claiming that the incident occurred in Bangladesh, alleging that Mahatma Gandhi’s statue was deliberately vandalised there. The image is being described as a recent incident and is being circulated across platforms with provocative and inflammatory captions.
Cyber Peace Foundation’s research and verification found that the claim being shared online is misleading. Our rsearch revealed that the viral image is not from Bangladesh. The image is actually from Chakulia in Uttar Dinajpur district of West Bengal, India
Claim:
Social media users claim that Mahatma Gandhi’s statue was vandalised in Bangladesh, and that the viral image shows a recent incident from the country.One Facebook user shared the video on 19 January 2026, making derogatory remarks and falsely linking the incident to Bangladesh. The post has since been widely shared on social media platforms. (Archived links and screenshots are available.)
Fact Check:
Our research revealed that the viral image is not from Bangladesh. The image is actually from Chakulia in Uttar Dinajpur district of West Bengal, India. To verify the claim, we conducted a reverse image search using Google Lens on key frames from the viral video. This led us to a report published by ABP Live Bangla on 16 January 2026, which featured the same visuals. Link and screenshot
According to ABP Live Bangla, the statue of Mahatma Gandhi was damaged during a protest in Chakulia. The statue’s head was found separated from the body. While a portion of the broken statue remained at the site on Thursday night, it was reported missing by Friday morning. The report further stated that extensive damage was observed at BDO Office No. 2 in Golpokhar. Gandhi’s statue, located at the entrance of the administrative building, was found broken, and ashes were discovered near the premises. Government staff were seen clearing scattered debris from the site.
The incident reportedly occurred during a SIR (Special Intensive Revision) hearing at the BDO office, which was disrupted due to vandalism. In connection with the violence and damage to government property, 21 people have been arrested so far. In the next stage of verification, we found the same footage in a 16 January 2026 report by local Bengali news channel K TV, which also showed clear visuals of the damaged Mahatma Gandhi statue. Link and screenshot.
Conclusion:
The viral image of Mahatma Gandhi’s broken statue does not depict an incident from Bangladesh. The image is from Chakulia in West Bengal’s Uttar Dinajpur district, where the statue was damaged during a protest.
Related Blogs
Executive Summary:
A video clip featuring Prime Minister Narendra Modi and the newly elected Bharatiya Janata Party (BJP) national president, Nitin Nabin, is going viral on social media. In the clip, PM Modi is seen apparently pushing Nitin Nabin, prompting claims that Nabin had accidentally stepped between the Prime Minister and the camera, after which Modi allegedly pushed him out of the frame. CyberPeace’s research found that the viral clip is misleading and cropped. The original, unedited video shows Prime Minister Modi gesturing for Nitin Nabin to move ahead and offer floral tributes to the statues of Bharatiya Jana Sangh founder Syama Prasad Mukherjee and Pandit Deendayal Upadhyaya at the BJP headquarters in Delhi. It is pertinent to note that on 20 January 2026, BJP leader Nitin Nabin was elected as the party’s national president. Several senior BJP leaders, including Prime Minister Narendra Modi, were present at the event. During his address, PM Modi remarked, “Nitin Nabin ji is my boss, and I am a party worker.” The statement received widespread attention, following which multiple videos linking to the remark began circulating on social media. A Facebook user shared the viral clip with a Hindi caption alleging that despite calling himself a “party worker,” PM Modi pushed his “boss” out of the camera frame. The post further mocked the position of BJP president, claiming it to be merely ceremonial. (Archived link)
To verify the claim, we conducted a reverse image and video search, which led us to a longer version of the video uploaded on news agency INS’s official X handle on 20 January 2026. The caption stated that PM Modi, BJP president Nitin Nabin, Defence Minister Rajnath Singh, Home Minister Amit Shah, Union Minister Nitin Gadkari and senior leader J.P. Nadda paid tributes to Syama Prasad Mukherjee and Pandit Deendayal Upadhyaya at the BJP headquarters.
In the full video, PM Modi and Nitin Nabin are seen walking together. PM Modi then requests Nitin Nabin to proceed first for the floral tribute, placing his hand on Nabin’s back as a gesture to move forward. The viral clip selectively cuts this moment out of context and loops it to create a misleading impression. The complete footage clearly shows that PM Modi asked Nitin Nabin to offer tributes first, after which other leaders followed. There is no indication whatsoever that Nitin Nabin was pushed out of the camera frame, as claimed in the viral posts. We also found the live broadcast of the ‘Bharatiya Janata Party Sangathan Parv’ on BJP’s official YouTube channel. The same visuals appear at the end of the live stream, further confirming that PM Modi was merely gesturing for Nitin Nabin to proceed first.
Additionally, photographs available on Nitin Nabin’s official X handle show him offering floral tributes ahead of PM Modi, who is seen standing behind and waiting.
Conclusion:
CyberPeace research confirms that the viral clip has been cropped and shared with a false narrative. In the original context, Prime Minister Narendra Modi was respectfully inviting BJP national president Nitin Nabin to move ahead and pay tributes, not pushing him out of the camera frame.
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 viral video (archive link) claims General Upendra Dwivedi, Chief of Army Staff (COAS), admitted to losing six Air Force jets and 250 soldiers during clashes with Pakistan. Verification revealed the footage is from an IIT Madras speech, with no such statement made. AI detection confirmed parts of the audio were artificially generated.
Claim:
The claim in question is that General Upendra Dwivedi, Chief of Army Staff (COAS), admitted to losing six Indian Air Force jets and 250 soldiers during recent clashes with Pakistan.
Fact Check:
Upon conducting a reverse image search on key frames from the video, it was found that the original footage is from IIT Madras, where the Chief of Army Staff (COAS) was delivering a speech. The video is available on the official YouTube channel of ADGPI – Indian Army, published on 9 August 2025, with the description:
“Watch COAS address the faculty and students on ‘Operation Sindoor – A New Chapter in India’s Fight Against Terrorism,’ highlighting it as a calibrated, intelligence-led operation reflecting a doctrinal shift. On the occasion, he also focused on the major strides made in technology absorption and capability development by the Indian Army, while urging young minds to strive for excellence in their future endeavours.”
A review of the full speech revealed no reference to the destruction of six jets or the loss of 250 Army personnel. This indicates that the circulating claim is not supported by the original source and may contribute to the spread of misinformation.
Further using AI Detection tools like Hive Moderation we found that the voice is AI generated in between the lines.
Conclusion:
The claim is baseless. The video is a manipulated creation that combines genuine footage of General Dwivedi’s IIT Madras address with AI-generated audio to fabricate a false narrative. No credible source corroborates the alleged military losses.
- Claim: AI-Generated Audio Falsely Claims COAS Admitted to Loss of 6 Jets and 250 Soldiers
- Claimed On: Social Media
- Fact Check: False and Misleading
