#FactCheck - AI-Generated Video of Monkey Falsely Linked to Hanuman Devotion
A video is being widely shared on social media showing a monkey, with users claiming that the animal is immersed in devotion to Lord Hanuman. The clip is being circulated with assertions that the monkey was seen participating in Hanuman Aarti. Cyber Peace Foundation’s research found that the viral claim is fake. Our investigation revealed that the video is not real and has been generated using artificial intelligence tools.
Claim
On January 6, 2026, Facebook users shared the viral video claiming, “A monkey was seen immersed in devotion during Hanuman Aarti.”
- Post link: https://www.facebook.com/reel/1261813845766976
- Archived link: https://archive.ph/anid5
Screenshots of the post can be seen below.
FactCheck:
When we closely examined the viral video, we noticed several visual inconsistencies. These anomalies raised suspicion that the video might be AI-generated. To verify this, we scanned the video using the AI detection tool Hive Moderation. According to the results, the video was found to be 97 percent AI-generated.
Further, we analysed the video using another AI detection tool, Sightengine. The tool’s assessment indicated that the viral video is 98 percent AI-generated.
Conclusion
Our investigation confirms that the viral video claiming to show a monkey immersed in devotion to Lord Hanuman is AI-generated and not real. The claim circulating on social media is false and misleading.
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Executive Summary:
A video of former Army Chief General Manoj Pande is going viral on social media with the claim that he attacked the Modi government, saying that supporting Israel is causing significant harm to the Indian Army. The research by CyberPeace revealed that the audio present in the viral video is AI-generated. No such statement was made in the original video.
Claim:
On social media platform X, while sharing the viral video, users wrote, “Delhi: Former Army Chief General Manoj Pande (Retd.) said, ‘Do you know what the biggest loss of supporting Israel is? Our Indian Army was always trained as a moral force, but the current situation is turning it into an ethnic force. Remember my words, this situation is moving towards a complete rebellion. We have all seen what is happening in Assam.’ ‘The Israeli army stands against humanity, and brutality has become its identity. Our army is becoming like them due to its association. The Modi government and the Sangh Parivar are responsible for this. For both, Israel is an ideal country, and they are running an agenda to turn India into Israel.’”
Fact Check:
In the research of the viral video claiming that former Army Chief General Manoj Pande attacked the Modi government, we conducted a reverse image search with the help of keyframes. During this process, we found a video uploaded on March 14 on the X account of the news agency Press Trust of India (PTI).
The visuals present in the video matched those in the viral video.
In this video, former Army Chief General Manoj Pande was seen delivering a speech in Marathi and English. However, during this, he was talking about increasing new kinds of capabilities in view of the current situation and not mentioning Israel, as claimed in the viral video. In the approximately 1 minute 15 seconds long video, he did not give any such statement as present in the viral video.
While taking the research forward, we found a report published on March 15, 2026, on the website of ThePrint. This report mentioned the speech delivered by former Army Chief General Manoj Pande, but no report mentioned the statement shown in the viral video.
Conclusion:
Our research found that the audio present in the viral video is AI-generated. In the original video, he did not make any such statement.
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Introduction
In the vast expanse of the digital cosmos, where the tendrils of the internet weave an intricate tapestry of connectivity, the channels through which information cascades have become a labyrinth of enigma and complexity. As we traverse this boundless virtual landscape, the line demarcating fact from fiction blurs, leaving the essence of truth adrift in a deluge of data. Amidst this ceaseless flow, platforms such as YouTube, Meta, and Twitter emerge as bulwarks in a pivotal struggle against the insidious spectres of fake news and disinformation—a struggle as fervent and consequential as any historical skirmish over the dominion of truth and influence.
Let us delve into a few case studies that illustrate the multifaceted nature of this digital warfare, where the stakes are nothing less than the integrity of public discourse and the sanctity of societal harmony.
Case 1: A Chief Minister's Stand Against Digital Deception
In the northeastern reaches of India, Assam's Chief Minister, Himanta Biswa Sarma, confronted disinformation head-on. With the spectre of elections looming like a storm on the horizon, he took to the microblogging site X to unveil a nefarious scheme—a doctored video intended to distort his speech and sow seeds of communal discord. 'See for yourself, as elections approach, how vested groups distort a speech with the criminal intention of spreading disinformation and communal disharmony. The long arms of the law will catch up with these elements,' declared Sarma, his words a clarion call for vigilance.
The counterfeit video, crafted to smear the Chief Minister's reputation, elicited a swift and decisive response from Assam's Director General of Police, G.P. Singh. 'Noted Sir. CID Assam would register a criminal case and investigate the people behind this,' assured Singh, signalling the readiness of the law to pursue the purveyors of falsehood.
Case 2: Waves of Deceit: Unverified Claims of Cancellations in the Maldives Tourism Controversy
The narrative shifts to the idyllic archipelago of the Maldives, where the azure waters belie a tumultuous undercurrent of diplomatic discord with India. Following disparaging remarks by Maldivian officials directed at Indian Prime Minister Narendra Modi, the social media sphere became rife with claims of Indian tourists en masse cancelling their sojourns to the island nation. Screenshots purporting to show cancelled bookings flooded platforms like X, with one user claiming to have annulled a reservation at the Palms Retreat, Fulhadhoo, to the tune of at least Rs 5 lakh, citing the officials' 'racist remarks.'
Initial reports from a few media outlets lent credence to this narrative of widespread cancellations. However, upon closer scrutiny, the veracity of these claims crumbled like a sandcastle at high tide. Concrete evidence to substantiate the alleged boycott was conspicuously absent, and neither travel agencies nor airlines corroborated the supposed trend.
The controversy was inflamed when PM Modi's visit to Lakshadweep, and subsequent social media posts praising the archipelago, spurred Indian users to champion Lakshadweep as an alternative to the Maldives. The vitriolic response from Maldivian ministers, who labelled Modi with derogatory remarks, ignited a firestorm on X, with hashtags like #BoycottMaldives and #MaldivesBoycott trending fervently.
Yet, the truth behind the cacophony of cancellation numbers remains shrouded in ambiguity, with no official acknowledgement from either government and a conspicuous absence of data from the tourism industry.
Case 3: Misinformation Highway: Unraveling the Fabrications in Bollywood's rumours or misinformation: Lies, Thumbnails, and Digital Dalliances
Gaze now turns to the bustling fabricated thumbnails or rumour taglines on uploaded videos on YouTube, where thumbnails emblazoned with tantalising texts beckon viewers with the promise of scandalous revelations. 'Pregnant? Divorced?' they shout, luring millions into their web with the allure of salacious 'news.' Yet, these are but mirages, baseless rumours masquerading as fact, or worse, complete fabrications.
The platform teems with counterfeit narratives and rumours, targeting the luminaries of Bollywood. Factors such as easy content uploading without strict scrutiny, a burgeoning digital footprint, and India's insatiable appetite for celebrity culture have created a fertile ground for the proliferation of such content. It is a testament to the power of the digital age, where anyone with a connection can craft a narrative and cast it into the ether, regardless of its foundation in reality.
We must arm ourselves with discernment and scepticism in this relentless onslaught of misinformation. The digital realm, for all its wonders, is also a battleground where the currency is truth, and the price of negligence is the erosion of our collective understanding. As we navigate this ever-evolving landscape, let us hold fast to the principles of verification and evidence, for they are the compass by which we can chart a course through the maelstrom of misinformation that seeks to engulf us.
Conclusion
In this era of digital enlightenment, it is incumbent upon us to discern the chaff from the wheat, to elevate the discourse beyond the mire of falsehoods. Let us endeavour to foster a digital polity that values truth, champions authenticity, and resolutely stands against the tide of disinformation that threatens to undermine the very fabric of our society.
References:
- https://www.indiatodayne.in/assam/video/assam-cm-exposes-fake-video-scheme-dgp-promises-swift-action-743097-2024-01-08
- https://www.thequint.com/news/webqoof/boycott-maldives-misinformation-on-trip-booking-cancellations
- https://www.thequint.com/news/webqoof/bollywood-fake-news-on-youtube-uses-divorce-pregnancy-and-arrests-for-misinformation
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
