#FactCheck - Manipulated Image Alleging Disrespect Towards PM Circulates Online
Executive Summary:
A manipulated image showing someone making an offensive gesture towards Prime Minister Narendra Modi is circulating on social media. However, the original photo does not display any such behavior towards the Prime Minister. The CyberPeace Research Team conducted an analysis and found that the genuine image was published in a Hindustan Times article in May 2019, where no rude gesture was visible. A comparison of the viral and authentic images clearly shows the manipulation. Moreover, The Hitavada also published the same image in 2019. Further investigation revealed that ABPLive also had the image.
Claims:
A picture showing an individual making a derogatory gesture towards Prime Minister Narendra Modi is being widely shared across social media platforms.
Fact Check:
Upon receiving the news, we immediately ran a reverse search of the image and found an article by Hindustan Times, where a similar photo was posted but there was no sign of such obscene gestures shown towards PM Modi.
ABP Live and The Hitavada also have the same image published on their website in May 2019.
Comparing both the viral photo and the photo found on official news websites, we found that almost everything resembles each other except the derogatory sign claimed in the viral image.
With this, we have found that someone took the original image, published in May 2019, and edited it with a disrespectful hand gesture, and which has recently gone viral across social media and has no connection with reality.
Conclusion:
In conclusion, a manipulated picture circulating online showing someone making a rude gesture towards Prime Minister Narendra Modi has been debunked by the Cyberpeace Research team. The viral image is just an edited version of the original image published in 2019. This demonstrates the need for all social media users to check/ verify the information and facts before sharing, to prevent the spread of fake content. Hence the viral image is fake and Misleading.
- Claim: A picture shows someone making a rude gesture towards Prime Minister Narendra Modi
- Claimed on: X, Instagram
- Fact Check: Fake & Misleading
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In the vast, interconnected cosmos of the internet, where knowledge and connectivity are celebrated as the twin suns of enlightenment, there lurk shadows of a more sinister nature. Here, in these darker corners, the innocence of childhood is not only exploited but also scarred, indelibly and forever. The production, distribution, and consumption of Child Sexual Abuse Material (CSAM) have surged to alarming levels globally, casting a long, ominous shadow over the digital landscape.
In response to this pressing issue, the National Human Rights Commission (NHRC) has unfurled a comprehensive four-part advisory, a beacon of hope aimed at combating CSAM and safeguarding the rights of children in this digital age. This advisory dated 27/10/23 is not merely a reaction to the rising tide of CSAM, but a testament to the imperative need for constant vigilance in the realm of cyber peace.
The statistics paint a sobering picture. In 2021, more than 1,500 instances of publishing, storing, and transmitting CSAM were reported, shedding a harsh light on the scale of the problem. Even more alarming is the upward trend in cases reported in subsequent years. By 2023, a staggering 450,207 cases of CSAM had already been reported, marking a significant increase from the 204,056 and 163,633 cases reported in 2022 and 2021, respectively.
The Key Aspects of Advisory
The NHRC's advisory commences with a fundamental recommendation - a redefinition of terminology. It suggests replacing the term 'Child Pornography' with 'Child Sexual Abuse Material' (CSAM). This shift in language is not merely semantic; it underscores the gravity of the issue, emphasizing that this is not about pornography but child abuse.
Moreover, the advisory calls for the definition of 'sexually explicit' under Section 67B of the IT Act, 2000. This step is crucial for ensuring the prompt identification and removal of online CSAM. By giving a clear definition, law enforcement can act swiftly in removing such content from the internet.
The digital world knows no borders, and CSAM can easily cross jurisdictional lines. NHRC recognizes this challenge and proposes that laws be harmonized across jurisdictions through bilateral agreements. Moreover, it recommends pushing for the adoption of a UN draft Convention on 'Countering the Use of Information and Communications Technologies for Criminal Purposes' at the General Assembly.
One of the critical aspects of the advisory is the strengthening of law enforcement. NHRC advocates for the creation of Specialized State Police Units in every state and union territory to handle CSAM-related cases. The central government is expected to provide support, including grants, to set up and equip these units.
The NHRC further recommends establishing a Specialized Central Police Unit under the government of India's jurisdiction. This unit will focus on identifying and apprehending CSAM offenders and maintaining a repository of such content. Its role is not limited to law enforcement; it is expected to cooperate with investigative agencies, analyze patterns, and initiate the process for content takedown. This coordinated approach is designed to combat the problem effectively, both on the dark web and open web.
The role of internet intermediaries and social media platforms in controlling CSAM is undeniable. The NHRC advisory emphasizes that intermediaries must deploy technology, such as content moderation algorithms, to proactively detect and remove CSAM from their platforms. This places the onus on the platforms to be proactive in policing their content and ensuring the safety of their users.
New Developments
Platforms using end-to-end encryption services may be required to create additional protocols for monitoring the circulation of CSAM. Failure to do so may invite the withdrawal of the 'safe harbor' clause under Section 79 of the IT Act, 2000. This measure ensures that platforms using encryption technology are not inadvertently providing safe havens for those engaged in illegal activities.
NHRC's advisory extends beyond legal and law enforcement measures; it emphasizes the importance of awareness and sensitization at various levels. Schools, colleges, and institutions are called upon to educate students, parents, and teachers about the modus operandi of online child sexual abusers, the vulnerabilities of children on the internet, and the early signs of online child abuse.
To further enhance awareness, a cyber curriculum is proposed to be integrated into the education system. This curriculum will not only boost digital literacy but also educate students about relevant child care legislation, policies, and the legal consequences of violating them.
NHRC recognizes that survivors of CSAM need more than legal measures and prevention strategies. Survivors are recommended to receive support services and opportunities for rehabilitation through various means. Partnerships with civil society and other stakeholders play a vital role in this aspect. Moreover, psycho-social care centers are proposed to be established in every district to facilitate need-based support services and organization of stigma eradication programs.
NHRC's advisory is a resounding call to action, acknowledging the critical importance of protecting children from the perils of CSAM. By addressing legal gaps, strengthening law enforcement, regulating online platforms, and promoting awareness and support, the NHRC aims to create a safer digital environment for children.
Conclusion
In a world where the internet plays an increasingly central role in our lives, these recommendations are not just proactive but imperative. They underscore the collective responsibility of governments, law enforcement agencies, intermediaries, and society as a whole in safeguarding the rights and well-being of children in the digital age.
NHRC's advisory is a pivotal guide to a more secure and child-friendly digital world. By addressing the rising tide of CSAM and emphasizing the need for constant vigilance, NHRC reaffirms the critical role of organizations, governments, and individuals in ensuring cyber peace and child protection in the digital age. The active contribution from premier cyber resilience firms like Cyber Peace Foundation, amplifies the collective action forging a secure digital space, highlighting the pivotal role played by think tanks in ensuring cyber peace and resilience.
References:
- https://www.hindustantimes.com/india-news/nhrc-issues-advisory-regarding-child-sexual-abuse-material-on-internet-101698473197792.html
- https://ssrana.in/articles/nhrcs-advisory-proliferation-of-child-sexual-abuse-material-csam/
- https://theprint.in/india/specialised-central-police-unit-use-of-technology-to-proactively-detect-csam-nhrc-advisory/1822223/
Executive Summary
A video is being widely shared on social media with the claim that Baloch people celebrated by dancing after Pakistan’s crushing defeat to India in the T20 World Cup. However, research by the CyberPeace found the claim to be misleading. The video is actually from a Lohri celebration held on January 23 at Government College University in Lahore, and is unrelated to any cricket match. India defeated Pakistan by 61 runs in the T20 World Cup 2026 match held in Colombo last Sunday. India scored 175 runs for the loss of seven wickets in 20 overs, while Pakistan were bowled out for 114 runs in 18 overs.
Claim
The 30-second video was shared on X with the caption, “Baloch people celebrate India’s victory.” The footage shows a group of men dressed in traditional attire dancing around a fire, while a large crowd gathers around and applauds.
Fact Check
To verify the authenticity of the viral claim, key frames from the video were extracted and subjected to reverse image search. The search led to an Instagram post uploaded on January 26, 2026, by an account associated with Government College University Lahore. The caption described the performance as a Balochistan cultural dance held at the university’s amphitheatre.
Further research also uncovered another video of the same event, recorded from a different angle and uploaded on January 24, 2026, on Instagram. The caption again confirmed that the event took place at Government College University Lahore.
Conclusion
The evidence confirms that the viral video does not show Baloch people celebrating Pakistan’s defeat in the T20 World Cup. Instead, it depicts a cultural dance performance during a Lohri celebration at Government College University Lahore, and has been shared with a misleading claim.
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
