#FactCheck - Misleading Video Allegedly Depicting Trampling of Indian Tri-colour in Kerala or Tamil Nadu Circulates on Social Media
Executive Summary:
The video that allegedly showed cars running into an Indian flag while Pakistan flags flying in the air in Indian states, went viral on social media but it has been established to be misleading. The video posted is neither from Kerala nor Tamil Nadu as claimed, instead from Karachi, Pakistan. There are specific details like the shop's name, Pakistani flags, car’s number plate, geolocation analyses that locate where the video comes from. The false information underscores the importance of verifying information before sharing it.
Claims:
A video circulating on social media shows cars trampling the Indian Tricolour painted on a road, as Pakistani flags are raised in pride, with the incident allegedly taking place in Tamil Nadu or Kerala.
Fact Check:
Upon receiving the post we closely watched the video, and found several signs that indicated the video was from Pakistan but not from any place in India.
We divided the video into keyframes and found a shop name near the road.
We enhanced the image quality to see the shop name clearly.
We can see that it’s written as ‘Sanam’, also we can see Pakistan flags waving on the road. Taking a cue from this we did some keyword searches with the shop name. We found some shops with the name and one of the shop's name ‘Sanam Boutique’ located in Karachi, Pakistan, was found to be similar when analyzed using geospatial Techniques.
We also found a similar structure of the building while geolocating the place with the viral video.
Additional confirmation of the place is the car’s number plate found in the keyframes of the video.
We found a website that shows the details of the number Plate in Karachi, Pakistan.
Upon thorough investigation, it was found that the location in the viral video is from Karachi, Pakistan, but not from Kerala or Tamil Nadu as claimed by different users in Social Media. Hence, the claim made is false and misleading.
Conclusion:
The video circulating on social media, claiming to show cars trampling the Indian Tricolour on a road while Pakistani flags are waved, does not depict an incident in Kerala or Tamil Nadu as claimed. By fact-checking methodologies, it has been confirmed now that the location in the video is actually from Karachi, Pakistan. The misrepresentation shows the importance of verifying the source of any information before sharing it on social media to prevent the spread of false narratives.
- Claim: A video shows cars trampling the Indian Tricolour painted on a road, as Pakistani flags are raised in pride, taking place in Tamil Nadu or Kerala.
- Claimed on: X (Formerly known as Twitter)
- Fact Check: Fake & Misleading
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Introduction:
This Op-ed sheds light on the perspectives of the US and China regarding cyber espionage. Additionally, it seeks to analyze China's response to the US accusation regarding cyber espionage.
What is Cyber espionage?
Cyber espionage or cyber spying is the act of obtaining personal, sensitive, or proprietary information from individuals without their knowledge or consent. In an increasingly transparent and technological society, the ability to control the private information an individual reveals on the Internet and the ability of others to access that information are a growing concern. This includes storage and retrieval of e-mail by third parties, social media, search engines, data mining, GPS tracking, the explosion of smartphone usage, and many other technology considerations. In the age of big data, there is a growing concern for privacy issues surrounding the storage and misuse of personal data and non-consensual mining of private information by companies, criminals, and governments.
Cyber espionage aims for economic, political, and technological gain. Fox example Stuxnet (2010) cyber-attack by the US and its allies Israel against Iran’s Nuclear facilities. Three espionage tools were discovered connected to Stuxnet, such as Gauss, FLAME and DuQu, for stealing data such as passwords, screenshots, Bluetooth, Skype functions, etc.
Cyber espionage is one of the most significant and intriguing international challenges globally. Many nations and international bodies, such as the US and China, have created their definitions and have always struggled over cyber espionage norms.
The US Perspective
In 2009, US officials (along with other allied countries) mentioned that cyber espionage was acceptable if it safeguarded national security, although they condemned economically motivated cyber espionage. Even the Director of National Intelligence said in 2013 that foreign intelligence capabilities cannot steal foreign companies' trade secrets to benefit their firms. This stance is consistent with the Economic Espionage Act (EEA) of 1996, particularly Section 1831, which prohibits economic espionage. This includes the theft of a trade secret that "will benefit any foreign government, foreign agent or foreign instrumentality.
Second, the US advocates for cybersecurity market standards and strongly opposes transferring personal data extracted from the US Office of Personnel Management (OPM) to cybercrime markets. Furthermore, China has been reported to sell OPM data on illicit markets. It became a grave concern for the US government when the Chinese government managed to acquire sensitive details of 22.1 million US government workers through cyber intrusions in 2014.
Third, Cyber-espionage is acceptable unless it’s utilized for Doxing, which involves disclosing personal information about someone online without their consent and using it as a tool for political influence operations. However, Western academics and scholars have endeavoured to distinguish between doxing and whistleblowing. They argue that whistleblowing, exemplified by events like the Snowden Leaks and Vault 7 disclosures, serves the interests of US citizens. In the US, being regarded as an open society, certain disclosures are not promoted but rather required by mandate.
Fourth, the US argues that there is no cyber espionage against critical infrastructure during peacetime. According to the US, there are 16 critical infrastructure sectors, including chemical, nuclear, energy, defence, food, water, and so on. These sectors are considered essential to the US, and any disruption or harm would impact security, national public health and national economic security.
The US concern regarding China’s cyber espionage
According to James Lewis (a senior vice president at the Center for US-China Economic and Security Review Commission), the US faces losses between $ 20 billion and $30 billion annually due to China’s cyberespionage. The 2018 U.S. Trade Representative (USTR) Section 301 report highlighted instances, where the Chinese government and executives from Chinese companies engaged in clandestine cyber intrusions to obtaining commercially valuable information from the U.S. businesses, such as in 2018 where officials from China’s Ministry of State Security, stole trade from General Electric aviation and other aerospace companies.
China's response to the US accusations of cyber espionage
China's perspective on cyber espionage is outlined by its 2014 anti-espionage law, which was revised in 2023. Article 1 of this legislation is formulated to prevent, halt, and punish espionage actions to maintain national security. Article 4 addresses the act of espionage and does not differentiate between state-sponsored cyber espionage for economic purposes and state-sponsored cyber espionage for national security purposes. However, China doesn't make a clear difference between government-to-government hacking (spying) and government-to-corporate sector hacking, unlike the US. This distinction is less apparent in China due to its strong state-owned enterprise (SOE) sector. However, military spying is considered part of the national interest in the US, while corporate spying is considered a crime.
China asserts that the US has established cyber norms concerning cyber espionage to normalize public attribution as acceptable conduct. This is achieved by targeting China for cyber operations, imposing sanctions on accused Chinese individuals, and making political accusations, such as blaming China and Russia for meddling in US elections. Despite all this, Washington D.C has never taken responsibility for the infamous Flame and Stuxnet cyber operations, which were widely recognized as part of a broader collaborative initiative known as Operation Olympic Games between the US and Israel. Additionally, the US takes the lead in surveillance activities conducted against China, Russia, German Chancellor Angela Merkel, the United Nations (UN) Secretary-General, and several French presidents. Surveillance programs such as Irritant Horn, Stellar Wind, Bvp47, the Hive, and PRISM are recognized as tools used by the US to monitor both allies and adversaries to maintain global hegemony.
China urges the US to cease its smear campaign associated with Volt Typhoon’s cyberattack for cyber espionage, citing the publication of a report titled “Volt Typhoon: A Conspiratorial Swindling Campaign Targets with U.S. Congress and Taxpayers Conducted by U.S. Intelligence Community” by China's National Computer Virus Emergency Response Centre and the 360 Digital Security Group on 15 April. According to the report, 'Volt Typhoon' is a ransomware cyber criminal group self-identified as the 'Dark Power' and is not affiliated with any state or region. Multiple cybersecurity authorities in the US collaborated to fabricate this story just for more budgets from Congress. In the meantime, Microsoft and other U.S. cybersecurity firms are seeking more big contracts from US cybersecurity authorities. The reality behind “Volt Typhoon '' is a conspiratorial swindling campaign to achieve two objectives by amplifying the "China threat theory" and cheating money from the U.S. Congress and taxpayers.
Beijing condemned the US claims of cyber espionage without any solid evidence. China also blames the US for economic espionage by citing the European Parliament report that the National Security Agency (NSA) was also involved in assisting Boeing in beating Airbus for a multi-billion dollar contract. Furthermore, Brazilian President Dilma Rousseff also accused the US authorities of spying against the state-owned oil company “Petrobras” for economic reasons.
Conclusion
In 2015, the US and China marked a milestone as both President Xi Jinping and Barack Obama signed an agreement, committing that neither country's government would conduct or knowingly support cyber-enabled theft of trade secrets, intellectual property, or other confidential business information to grant competitive advantages to firms or commercial sectors. However, the China Cybersecurity Industry Alliance (CCIA) published a report titled 'US Threats and Sabotage to the Security and Development of Global Cyberspace' in 2024, highlighting the US escalating cyber-attack and espionage activities against China and other nations. Additionally, there has been a considerable increase in the volume and sophistication of Chinese hacking since 2016. According to a survey by the Center for International and Strategic Studies, out of 224 cyber espionage incidents reported since 2000, 69% occurred after Xi assumed office. Therefore, China and the US must address cybersecurity issues through dialogue and cooperation, utilizing bilateral and multilateral agreements.
A video circulating widely on social media claims that Defence Minister Rajnath Singh compared the Rashtriya Swayamsevak Sangh (RSS) with the Afghan Taliban. The clip allegedly shows Singh stating that both organisations share a common ideology and belief system and therefore “must walk together.” However, a research by the CyberPeace found that the video is digitally manipulated, and the audio attributed to Rajnath Singh has been fabricated using artificial intelligence.
Claim
An X user, Aamir Ali Khan (@Aamir_Aali), on January 20 shared a video of Defence Minister Rajnath Singh, claiming that he drew parallels between the Rashtriya Swayamsevak Sangh (RSS) and the Afghan Taliban. The user alleged that Singh stated both organisations follow a similar ideology and belief system and therefore must “walk together.” The post further quoted Singh as allegedly saying: “Indian RSS & Afghan Taliban have one ideology, we have one faith, we have one alliance, our mutual enemy is Pakistan. Israel is a strategic partner of India & Afghan Taliban are Israeli friends. We must join hands to destroy the enemy Pakistan.” Here is the link and archive link to the post, along with a screenshot.
Fact Check:
To verify the claim, the CyberPeace conducted a Google Lens search using keyframes extracted from the viral video. This search led to an extended version of the same footage uploaded on the official YouTube channel of Rajnath Singh. The original video was traced back to the inaugural ceremony of the Medium Calibre Ammunition Facility, constructed by Solar Industries in Nagpur. Upon reviewing the complete, unedited speech, the Desk found no instance where Rajnath Singh made any remarks comparing the RSS with the Afghan Taliban or spoke about shared ideology, alliances, or Pakistan in the manner claimed.
In the authentic footage, the Defence Minister spoke about:
" India’s push for Aatmanirbharta (self-reliance) in defence manufacturing
Strengthening domestic ammunition production
Positioning India as a global hub for defence exports "
The statements attributed to him in the viral clip were entirely absent from the original speech.
Here is the link to the original video, along with a screenshot.
In the next stage of the research , the audio track from the viral video was extracted and analysed using the AI voice detection tool Aurigin. This confirmed that the original visuals were misused and overlaid with a synthetic voice track to create a misleading narrative.
Conclusion
The CyberPeace concluded that the viral video claiming Defence Minister Rajnath Singh compared the RSS with the Afghan Taliban is false and misleading. The video has been digitally manipulated, with an AI-generated audio track falsely attributed to Singh. The Defence Minister made no such remarks during the Nagpur event, and the claim circulating online is fabricated.
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
