#FactCheck- Viral Image of Rescued U.S. Airman in Iran is AI-Generated

Introduction

With the rise of AI deepfakes and manipulated media, it has become difficult for the average internet user to know what they can trust online. Synthetic media can have serious consequences, from virally spreading election disinformation or medical misinformation to serious consequences like revenge porn and financial fraud. Recently, a Pune man lost ₹43 lakh when he invested money based on a deepfake video of Infosys founder Narayana Murthy. In another case, that of Babydoll Archi, a woman from Assam had her likeness deepfaked by an ex-boyfriend to create revenge porn.

Image or video manipulation used to leave observable traces. Online sources may advise examining the edges of objects in the image, checking for inconsistent patterns, lighting differences, observing the lip movements of the speaker in a video or counting the number of fingers on a person’s hand. Unfortunately, as the technology improves, such folk advice might not always help users identify synthetic and manipulated media.

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The Coalition for Content Provenance and Authenticity (C2PA)

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One interesting project in the area of trust-building under these circumstances has been the Coalition for Content Provenance and Authenticity (C2PA). Started in 2019 by Adobe and Microsoft, C2PA is a collaboration between major players in AI, social media, journalism, and photography, among others. It set out to create a standard for publishers of digital media to prove the authenticity of digital media and track changes as they occur.

When photos and videos are captured, they generally store metadata like the date and time of capture, the location, the device it was taken on, etc. C2PA developed a standard for sharing and checking the validity of this metadata, and adding additional layers of metadata whenever a new user makes any edits. This creates a digital record of any and all changes made. Additionally, the original media is bundled with this metadata. This makes it easy to verify the source of the image and check if the edits change the meaning or impact of the media. This standard allows different validation software, content publishers and content creation tools to be interoperable in terms of maintaining and displaying proof of authenticity.

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The standard is intended to be used on an opt-in basis and can be likened to a nutrition label for digital media. Importantly, it does not limit the creativity of fledgling photo editors or generative AI enthusiasts; it simply provides consumers with more information about the media they come across.

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Could C2PA be Useful in an Indian Context?

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The World Economic Forum’s Global Risk Report 2024, identifies India as a significant hotspot for misinformation. The recent AI Regulation report by MeitY indicates an interest in tools for watermarking AI-based synthetic content for ease of detecting and tracking harmful outcomes. Perhaps C2PA can be useful in this regard as it takes a holistic approach to tracking media manipulation, even in cases where AI is not the medium.

Currently, 26 India-based organisations like the Times of India or Truefy AI have signed up to the Content Authenticity Initiative (CAI), a community that contributes to the development and adoption of tools and standards like C2PA. However, people are increasingly using social media sites like WhatsApp and Instagram as sources of information, both of which are owned by Meta and have not yet implemented the standard in their products.

India also has low digital literacy rates and low resistance to misinformation. Part of the challenge would be showing people how to read this nutrition label, to empower people to make better decisions online. As such, C2PA is just one part of an online trust-building strategy. It is crucial that education around digital literacy and policy around organisational adoption of the standard are also part of the strategy.

The standard is also not foolproof. Current iterations may still struggle when presented with screenshots of digital media and other non-technical digital manipulation. Linking media to their creator may also put journalists and whistleblowers at risk. Actual use in context will show us more about how to improve future versions of digital provenance tools, though these improvements are not guarantees of a safer internet.

The largest advantage of C2PA adoption would be the democratisation of fact-checking infrastructure. Since media is shared at a significantly faster rate than it can be verified by professionals, putting the verification tools in the hands of people makes the process a lot more scalable. It empowers citizen journalists and leaves a public trail for any media consumer to look into.

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Conclusion

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From basic colour filters to make a scene more engaging, to removing a crowd from a social media post, to editing together videos of a politician to make it sound like they are singing a song, we are so accustomed to seeing the media we consume be altered in some way. The C2PA is just one way to bring transparency to how media is altered. It is not a one-stop solution, but it is a viable starting point for creating a fairer and democratic internet and increasing trust online. While there are risks to its adoption, it is promising to see that organisations across different sectors are collaborating on this project to be more transparent about the media we consume.

References

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• https://c2pa.org/
• https://contentauthenticity.org/
• https://indianexpress.com/article/technology/tech-news-technology/kate-middleton-9-signs-edited-photo-9211799/  
• https://photography.tutsplus.com/articles/fakes-frauds-and-forgeries-how-to-detect-image-manipulation--cms-22230
• https://www.media.mit.edu/projects/detect-fakes/overview/
• https://www.youtube.com/watch?v=qO0WvudbO04&pp=0gcJCbAJAYcqIYzv
• https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2024.pdf
• https://indianexpress.com/article/technology/tech-news-technology/ai-law-may-not-prescribe-penal-consequences-for-violations-9457780/
• https://thesecretariat.in/article/meity-s-ai-regulation-report-ambitious-but-no-concrete-solutions
• https://www.ndtv.com/lifestyle/assam-what-babydoll-archi-viral-fame-says-about-india-porn-problem-8878689
• https://www.meity.gov.in/static/uploads/2024/02/9f6e99572739a3024c9cdaec53a0a0ef.pdf

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Introduction

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Cyberwarfare has evolved into one of the most decisive instruments of statecraft and conflict. The increasing digitisation of critical infrastructure like power grids, water systems, transportation systems, healthcare networks, and energy sources has made these systems new targets in the war of algorithms. Military logic is evolving to paralyse the nation’s critical infrastructure to keep its resources engaged in repairing them and thereby break the nation’s ability to deter and counter attacks, all without firing a single bullet. 

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From Ransomware to an Invisible Sabotage: The changing nature of warfare

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The operational technology (OT) landscape has become the epicentre of cyber operations, all around the world. Once, which was insulated, related to industrial systems that controlled turbines, pipelines, or dams, they now stand connected to the Internet through supervisory control and data acquisition (SCADA) and the Internet of Things. These connections have also become gateways for attackers, besides enhancing the efficiency of the infrastructural lifelines of the nation.   

Groups like Volt Typhoon, Sandworm, Laurionite, and Cyberavengers have transformed the art of digital infiltration into a strategic shift. Volt Typhoon, which is linked to China, has used “living-off-the-land” techniques to exploit the legitimate administrative tools to remain invisible while scanning the critical infrastructures in the US. Sandworm, which is aligned with Russia’s GRU (Glavnoye Razvedyvatelnoye Upravlenie) or Main Intelligence Directorate (in English), has demonstrated the power of cyber sabotage in real time, as its attacks on Ukraine’s power grids in 2015 and 2021 had left millions in darkness, coinciding with kinetic missile strikes. Meanwhile, the Iranian-affiliated Cyberavengers group, which has weaponised the AI-assisted malware, such as IOCONTROL, that are capable of hijacking water and energy control systems. Each of these systems used in these operations reflects a shift from direct espionage activities to a state of strategic paralysis. 

In comparison to the traditional cybercrime activities that are aimed at stealing data and extortion of money, these campaigns repeatedly target the physical systems, which consist of the machinery that sustains civilian life and military preparedness.  

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The Military Logic behind Cyber Targeting: A Web of Vulnerabilities 

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A critical infrastructure is a complex ecosystem that covers power generation, transportation, communication, and manufacturing are all interconnected, which means a single compromised node can cascade into a national paralysis. For instance, a breach in the systems of the dam can flood an entire city, a grid shutdown can halt water supply to hospitals, and even affect air traffic. The 2015 Black Energy Malware attack in Ukraine has proved this possibility when three utilities were hacked, plunging thousands of homes into darkness. The Iranian hackers once again gained access to the Bowman Avenue Dam of New York and controlled its floodgates, which gave a chilling demonstration of the destructive reality of digital manipulation. 

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The systems remain vulnerable mainly for 3 reasons such as- 

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• Legacy Architectures: Many of these industrial systems were designed decades ago with no built-in cybersecurity mechanisms. 
• Slow Patching and Segmentation Gaps: All updates and segmentation between IT and TO networks often lag, providing open entry points for attackers. 
• Converging with IoT: The integration of smart sensors and cloud-based management tools has expanded the attack surface exponentially. 

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This interconnected fragility has turned our critical infrastructures into both a weapon and a target or a tool for coercion in modern hybrid warfare. Between 2023 and 2024, over 420 cyberattacks were witnessed in several critical global infrastructures, which averaged to 13 attacks per second, according to a news report. These were not just random acts of digital vandalism; they were deliberate and coordinated operational attempts by state-led actors from China, Russia, and Iran. 

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Developing a new Resilience as the new tool of Deterrence 

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Cyber deterrence no longer rests on the fear of retaliation, it relies on the need for resilience. Nations that can absorb attacks, maintain continuity, and recover rapidly would be the true superpowers of this digital age. Segmentation, real-time threat detection, and AI-assisted recovery models are vital pillars of this model of resilience. The logic of modern cyberwarfare is clear, which means that the more a nation digitizes, the more it will need to defend itself. 

However, as the line between war and peace blurs, safeguarding critical infrastructure is no longer just an IT priority; rather, it is a national security doctrine. In this silent theatre of cyberwarfare, survival will depend not only on firepower, but on firewalls. 

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References

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• https://rmcglobal.com/critical-infrastructure-under-siege-the-top-ot-threats-of-2025/
• https://ccdcoe.org/uploads/2018/10/Geers2009_The-Cyber-Threat-to-National-Critical-Infrastructures.pdf
• https://www.researchgate.net/publication/335752979_Cybersecurity_of_Critical_Infrastructure 
• https://arxiv.org/html/2510.04118v1 ‍
• https://www.anapaya.net/blog/top-5-critical-infrastructure-cyberattacks

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.

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Why Bias in Training Data Matters

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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)

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Statistical Principles for Reducing Training Data Bias 

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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.

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Balancing Data Through Re-Sampling and Re-Weighting

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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)

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Feature Engineering and Data Transformation

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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)

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Measuring Fairness With Metrics

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Statistical fairness measures are used to measure the effectiveness of a model in groups. 

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Fairness-Aware Algorithms Explained

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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.

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Pre-Processing Techniques

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Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:

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• 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)

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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.

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In-Processing Techniques

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The in-processing techniques alter the learning algorithm. These include:

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• 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 

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Post-Processing Techniques

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Fairness may be enhanced after training by changing the model outputs. These strategies comprise:

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• 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)

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Challenges 

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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)

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Why Fairness-Aware Development Matters

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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.

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Conclusion 

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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.

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References

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• 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

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