#FactCheck - Stunning 'Mount Kailash' Video Exposed as AI-Generated Illusion!
EXECUTIVE SUMMARY:
A viral video is surfacing claiming to capture an aerial view of Mount Kailash that has breathtaking scenery apparently providing a rare real-life shot of Tibet's sacred mountain. Its authenticity was investigated, and authenticity versus digitally manipulative features were analyzed.
CLAIMS:
The viral video claims to reveal the real aerial shot of Mount Kailash, as if exposing us to the natural beauty of such a hallowed mountain. The video was circulated widely in social media, with users crediting it to be the actual footage of Mount Kailash.
FACTS:
The viral video that was circulated through social media was not real footage of Mount Kailash. The reverse image search revealed that it is an AI-generated video created by Sonam and Namgyal, two Tibet based graphic artists on Midjourney. The advanced digital techniques used helped to provide a realistic lifelike scene in the video.
No media or geographical source has reported or published the video as authentic footage of Mount Kailash. Besides, several visual aspects, including lighting and environmental features, indicate that it is computer-generated.
For further verification, we used Hive Moderation, a deep fake detection tool to conclude whether the video is AI-Generated or Real. It was found to be AI generated.
CONCLUSION:
The viral video claiming to show an aerial view of Mount Kailash is an AI-manipulated creation, not authentic footage of the sacred mountain. This incident highlights the growing influence of AI and CGI in creating realistic but misleading content, emphasizing the need for viewers to verify such visuals through trusted sources before sharing.
- Claim: Digitally Morphed Video of Mt. Kailash, Showcasing Stunning White Clouds
- Claimed On: X (Formerly Known As Twitter), Instagram
- Fact Check: AI-Generated (Checked using Hive Moderation).
Related Blogs
Executive Summary:
A video is being circulated on social media claiming that veteran actor Mithun Chakraborty has passed away. The viral clip shows actors Salman Khan and Govinda leaving in their respective cars. Social media users are sharing this footage while claiming that the actors were seen after the alleged demise of Mithun Chakraborty. However, research by the CyberPeace found the claim to be false. Our research revealed that Mithun Chakraborty is completely healthy and the claim about his death is misleading.
Claim
On March 6, 2026, an Instagram user shared the viral video claiming that actor Mithun Chakraborty had passed away.
Fact Check:
To verify the claim, we searched relevant keywords on Google. During this search, we did not find any credible media report confirming the death of Mithun Chakraborty.
We further checked the social media accounts of Mithun Chakraborty’s son Mahaakshay Chakraborty, but found no information related to the viral claim. This indicated that the posts circulating on social media about the actor’s death were false. To trace the origin of the viral footage, we extracted key frames from the video showing Salman Khan and Govinda and conducted a reverse image search using Google Lens. During the search, we found the clip featuring Salman Khan on the Facebook page of Times Now, posted on November 11, 2025.
According to the report, Salman Khan and Shah Rukh Khan were seen leaving Breach Candy Hospital in Mumbai, where veteran actor Dharmendra had been admitted on November 10 after his health suddenly deteriorated. The clip featuring Govinda was also found on the website of Navbharat Times, published on November 11, 2025. According to the report, Govinda had arrived late at night at Breach Candy Hospital to visit Dharmendra after he was hospitalized due to health issues.
Conclusion
Our research found that the viral claim is false. Actor Mithun Chakraborty is alive and healthy. The video circulating on social media actually shows Salman Khan and Govinda visiting Breach Candy Hospital in November 2025 when actor Dharmendra was admitted, and it has been wrongly linked to Mithun Chakraborty’s death.
Executive Summary:
A number of false information is spreading across social media networks after the users are sharing the mistranslated video with Indian Hindus being congratulated by Italian Prime Minister Giorgia Meloni on the inauguration of Ram Temple in Ayodhya under Uttar Pradesh state. Our CyberPeace Research Team’s investigation clearly reveals that those allegations are based on false grounds. The true interpretation of the video that actually is revealed as Meloni saying thank you to those who wished her a happy birthday.
Claims:
A X (Formerly known as Twitter) user’ shared a 13 sec video where Italy Prime Minister Giorgia Meloni speaking in Italian and user claiming to be congratulating India for Ram Mandir Construction, the caption reads,
“Italian PM Giorgia Meloni Message to Hindus for Ram Mandir #RamMandirPranPratishta. #Translation : Best wishes to the Hindus in India and around the world on the Pran Pratistha ceremony. By restoring your prestige after hundreds of years of struggle, you have set an example for the world. Lots of love.”
Fact Check:
The CyberPeace Research team tried to translate the Video in Google Translate. First, we took out the transcript of the Video using an AI transcription tool and put it on Google Translate; the result was something else.
The Translation reads, “Thank you all for the birthday wishes you sent me privately with posts on social media, a lot of encouragement which I will treasure, you are my strength, I love you.”
With this we are sure that it was not any Congratulations message but a thank you message for all those who sent birthday wishes to the Prime Minister.
We then did a reverse Image Search of frames of the Video and found the original Video on the Prime Minister official X Handle uploaded on 15 Jan, 2024 with caption as, “Grazie. Siete la mia” Translation reads, “Thank you. You are my strength!”
Conclusion:
The 13 Sec video shared by a user had a great reach at X as a result many users shared the Video with Similar Caption. A Misunderstanding starts from one Post and it spreads all. The Claims made by the X User in Caption of the Post is totally misleading and has no connection with the actual post of Italy Prime Minister Giorgia Meloni speaking in Italian. Hence, the Post is fake and Misleading.
- Claim: Italian Prime Minister Giorgia Meloni congratulated Hindus in the context of Ram Mandir
- Claimed on: X
- Fact Check: Fake
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
