#FactCheck- Viral video of burning ship falsely linked to US attack on vessel carrying Indian nationals in Oman waters
Executive Summary
Amid rising tensions in West Asia and concerns over the safety of Indian seafarers, a video showing a vessel engulfed in flames and thick black smoke is being widely circulated on social media. The clip is being shared with the claim that it depicts a US attack on a ship carrying Indian nationals in Omani waters. CyberPeace Research Wing research found the claim to be misleading. The viral video predates the incident it is being linked to and is not related to any recent attack.
Claim:
A Facebook user shared the video alleging that the United States attacked a ship carrying Indian citizens in the maritime region near Oman. The post has been widely circulated as part of the ongoing West Asia tensions. Facebook link: https://www.facebook.com/reel/3031094270427837, https://www.facebook.com/reel/3031094270427837
Fact Check:
A reverse image search of keyframes from the viral video led to several media reports published on 2 March 2026, which carried the same visuals seen in the circulating clip. The reports indicated that the footage showed a vessel targeted amid escalating tensions in the Strait of Hormuz, following restrictions on maritime movement in the region. https://www.ndtv.com/world-news/iran-targets-6th-ship-in-hormuz-strait-video-shows-billowing-smoke-11159019
Further research also found the same visuals in a Moneycontrol report published on 2 March 2026. The report clearly established that the footage is from an earlier incident and not linked to any recent developments. https://www.moneycontrol.com/world/indian-crew-member-killed-after-drone-boat-strikes-oil-tanker-off-oman-coast-first-indian-casualty-in-us-iran-tensions-article-13848303.html
Conclusion:
The research confirms that the viral video is not related to any recent US attack on a vessel carrying Indian nationals. The footage has been online since at least March 2026 and shows an earlier incident involving the oil tanker Skylight during tensions in the Strait of Hormuz. Hence, the claim being circulated on social media is misleading.
Related Blogs
Executive Summery
A video is being widely shared on social media claiming that a man seen working at a brick kiln is an Australian citizen named “Anderson.” The claim states that he lost his passport and all his belongings after a theft in Agra, following which he was forced to work at a brick kiln for survival. Social media users are circulating the video as a shameful incident and are also appealing for the return of his stolen passport and belongings so that he can safely return to his country. CyberPeace Research Wing research found that the viral story is completely false and fabricated. For verification, we first conducted a keyword-based search on Google, but did not find any credible news report or media coverage supporting such an incident. Had this event been true, it would have certainly been reported by mainstream media outlets.
Claim
Facebook user ‘Yadav Roshni’ posted a video on June 23, 2026 (archive link) along with a caption claiming that an Australian tourist named “Anderson” visited Agra (Uttar Pradesh) to explore India’s culture and heritage, but was allegedly robbed during his visit. The post claims that thieves stole his passport, money, and all his belongings, leaving him helpless in a foreign country It further alleges that after losing everything, the tourist was forced to work as a labourer at a brick kiln to survive. The post describes the incident as shameful and appeals to users to widely share the video so that it reaches authorities, and requests that whoever stole his belongings return them so that he can safely return to his home country.
https://www.facebook.com/reel/1001213176107998 , https://perma.cc/MB8S-ZD24?type=standard
Fact Check
We then extracted keyframes from the viral video and performed a Google Lens search. This led us to an Instagram video posted by user sahildeshwal7500 on June 25, 2026. In this video, the person seen in the viral clip is identified as a resident of Katha village in Baghpat, Uttar Pradesh, named Sahil Deshwal. He himself clarifies in the video that the viral claim is false and that his footage was misused and shared with a fake narrative.
https://www.instagram.com/reels/DaAEB7wJkG-/
Further, another Instagram user dipendrakiduniya also posted clarification on June 25, 2026. In the video, the same individual and his brother clearly deny the “Anderson” identity claim and confirm that the viral story is completely false. They state that he is an Indian labourer working at a brick kiln in Baghpat.
https://www.instagram.com/reels/DaAZgXtjaYI/
Conclusion
The viral claim that an Australian citizen named Anderson lost his passport and belongings in Agra and was forced to work at a brick kiln is completely false. The person seen in the video is a resident of Baghpat, Uttar Pradesh. The story circulating on social media is fabricated and misleading.
Artificial intelligence is revolutionizing industries such as healthcare to finance to influence the decisions that touch the lives of millions daily. However, there is a hidden danger associated with this power: unfair results of AI systems, reinforcement of social inequalities, and distrust of technology. One of the main causes of this issue is training data bias, which appears when the examples on which an AI model is trained are not representative or skewed. To deal with it successfully, this needs a combination of statistical methods, algorithmic design that is mindful of fairness, and robust governance over the AI lifecycle. This article discusses the origin of bias, the ways to reduce it, and the unique position of fairness-conscious algorithms.
Why Bias in Training Data Matters
The bias in AI occurs when the models mirror and reproduce the trends of inequality in the training data. When a dataset has a biased representation of a demographic group or includes historical biases, the model will be trained to make decisions in ways that will harm the group. This is a fact that has a practical implication: prejudiced AI may cause discrimination during the recruitment of employees, lending, and evaluation of criminal risks, as well as various other spheres of social life, thus compromising justice and equity. These problems are not only technical in nature but also require moral principles and a system of governance (E&ICTA).
Bias is not uniform. It may be based on the data itself, the algorithm design, or even the lack of diversity among developers. The bias in data occurs when data does not represent the real world. Algorithm bias may arise when design decisions inadvertently put one group at an unfair advantage over another. Both the interpretation of the model and data collection may be affected by human bias. (MDPI)
Statistical Principles for Reducing Training Data Bias
Statistical principles are at the core of bias mitigation and they redefine the data-model interaction. These approaches are focused on data preparation, training process adjustment, and model output corrections in such a way that the notion of fairness becomes a quantifiable goal.
Balancing Data Through Re-Sampling and Re-Weighting
Among the aforementioned methods, a fair representation of all the relevant groups in the dataset is one way. This can be achieved by oversampling underrepresented groups and undersampling overrepresented groups. Oversampling gives greater weight to minority examples, whereas re-weighting gives greater weight to under-represented data points in training. The methods minimize the tendency of models to fit to salient patterns and improve coverage among vulnerable groups. (GeeksforGeeks)
Feature Engineering and Data Transformation
The other statistical technique is to convert data characteristics in such a way that sensitive characteristics have a lesser impact on the results. In one example, fair representation learning adjusts the data representation to discourage bias during the untraining of the model. The disparate impact remover adjust technique performs the adjustment of features of the model in such a way that the impact of sensitive features is reduced during learning. (GeeksforGeeks)
Measuring Fairness With Metrics
Statistical fairness measures are used to measure the effectiveness of a model in groups.
Fairness-Aware Algorithms Explained
Fair algorithms do not simply detect bias. They incorporate fairness goals in model construction and run in three phases including pre-processing, in-processing, and post-processing.
Pre-Processing Techniques
Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:
- Rebalancing training data through sampling and re-weighting training data to address sample imbalances.
- Data augmentation to generate examples of underrepresented groups.
- Feature transformation removes or downplays the impact of sensitive attributes prior to the commencement of training. (IJMRSET)
These methods can be used to guarantee that the model is trained on more balanced data and to reduce the chances of bias transfer between historical data.
In-Processing Techniques
The in-processing techniques alter the learning algorithm. These include:
- Fairness constraints that penalize the model for making biased predictions during training.
- Adversarial debiasing, where a second model is used to ensure that sensitive attributes are not predicted by the learned representations.
- Fair representation learning that modifies internal model representations in favor of
Post-Processing Techniques
Fairness may be enhanced after training by changing the model outputs. These strategies comprise:
- Threshold adjustments to various groups to meet conditions of fairness, like equalized odds.
- Calibration techniques such that the estimated probabilities are fair indicators of the actual probabilities in groups. (GeeksforGeeks)
Challenges
Mitigating bias is complex. The statistical bias minimization may at times come at the cost of the model accuracy, and there is a conflict between predictive performance and fairness. The definition of fairness itself is potentially a difficult task because various applications of fairness require various criteria, and various criteria can be conflicting. (MDPI)
Gaining varied and representative data is also a challenge that is experienced because of privacy issues, incomplete records, and a lack of resources. The auditing and reporting done on a continuous basis are needed so that mitigation processes are up to date, as models are continually updated. (E&ICTA)
Why Fairness-Aware Development Matters
The outcomes of the unfair treatment of some groups by AI systems are far-reaching. Discriminatory software in recruitment may support inequality in the workplace. Subjective credit rating may deprive deserving people of opportunities. Unbiased medical forecasts might result in the flawed allocation of medical resources. In both cases, prejudice contravenes the credibility and clouds the greater prospect of AI. (E&ICTA)
Algorithms that are fair and statistical mitigation plans provide a way to create not only powerful AI but also fair and trustworthy AI. They admit that the results of AI systems are social tools whose effects extend across society. Responsible development will necessitate sustained fairness quantification, model adjustment, and upholding human control.
Conclusion
AI bias is not a technical malfunction. It is a mirror of real-world disparities in data and exaggerated by models. Statistical rigor, wise algorithm design, and readiness to address the trade-offs between fairness and performance are required to reduce training data bias. Fairness-conscious algorithms (which can be implemented in pre-processing, in-processing, or post-processing) are useful in delivering more fair results. As AI is taking part in the most crucial decisions, it is necessary to consider fairness at the beginning to have a system that serves the population in a responsible and fair manner.
References
- Understanding Bias in Artificial Intelligence: Challenges, Impacts, and Mitigation Strategies: E&ICTA, IITK
- Bias and Fairness in Artificial Intelligence: Methods and Mitigation Strategies: JRPS Shodh Sagar
- Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies: MDPI
- Ensuring Fairness in Machine Learning Algorithms: GeeksforGeeks
Bias and Fairness in Machine Learning Models: A Critical Examination of Ethical Implications: IJMRSET - Bias in AI Models: Origins, Impact, and Mitigation Strategies: Preprints
- Bias in Artificial Intelligence and Mitigation Strategies: TCS
- Survey on Machine Learning Biases and Mitigation Techniques: MDPI
Executive Summary
A video showing uniformed personnel removing street vendors and damaging roadside stalls is being circulated on social media with the false claim that the Nepal government has started expelling Bangladeshis from the country. The viral clip is around 49 seconds long and shows authorities clearing vendors from a public area. Nepali audio can also be heard in the background, which appears to have added to the misleading narrative.
However, an research by the CyberPeace Research Wing found that the claim is false. The video is not from Nepal, but from Bangladesh’s capital Dhaka, where authorities were carrying out an anti-encroachment operation near the National Stadium.
Claim:
The video was shared on X with a caption claiming that a Hindu-led government in Nepal had begun driving Bangladeshis out of the country.
Fact Check:
A close examination of the video revealed several signs contradicting the claim. Text written in Bengali can be seen at multiple places in the footage. The caps worn by uniformed personnel also appear to display “Bangladesh Police.” Investigators also noticed a stadium-like structure in the background. Comparing the visuals with street-view imagery available on Google Maps led to a match near the National Stadium area in Dhaka, Bangladesh.
Further verification found a video uploaded on March 18, 2025, by a Bangladeshi news portal on YouTube containing scenes matching the viral clip. The caption described it as footage from an anti-encroachment drive in the Gulistan area of Dhaka.
A similar version of the video was also found on a Bangladeshi Instagram account uploaded on March 17, where it was again described as police action against illegal encroachments. The video credit was attributed to a person named Zahir Rehan.
While the exact date of the original footage could not be independently confirmed, available evidence clearly links the video to Bangladesh and not Nepal.
Conclusion:
The viral claim that Nepal has begun expelling Bangladeshis is false. The video actually shows an anti-encroachment drive carried out by authorities in Dhaka, Bangladesh.
