#FactCheck-AI-Generated Image Falsely Shows Iranian Soldiers Near Downed Helicopter

A report by MarketsandMarkets in 2024 showed that the global AI market size is estimated to grow from USD 214.6 billion in 2024 to USD 1,339.1 billion in 2030, at a CAGR of 35.7%. AI has become an enabler of productivity and innovation. A Forbes Advisor survey conducted in 2023 reported that 56% of businesses use AI to optimise their operations and drive efficiency. Further, 51% use AI for cybersecurity and fraud management, 47% employ AI-powered digital assistants to enhance productivity and 46% use AI to manage customer relationships.

AI has revolutionised business functions. According to a Forbes survey, 40% of businesses rely on AI for inventory management, 35% harness AI for content production and optimisation and 33% deploy AI-driven product recommendation systems for enhanced customer engagement.  This blog addresses the opportunities and challenges posed by integrating AI into operational efficiency.

Artificial Intelligence and its resultant Operational Efficiency

AI has exemplary optimisation or efficiency capabilities and is widely used to do repetitive tasks. These tasks include payroll processing, data entry, inventory management, patient registration, invoicing, claims processing, and others. AI use has been incorporated into such tasks as it can uncover complex patterns using NLP, machine learning, and deep learning beyond human capabilities. It has also shown promise in improving the decision-making process for businesses in time-critical, high-pressure situations. 

AI-driven efficiency is visible in industries such as the manufacturing industry for predictive maintenance, in the healthcare industry for streamlining diagnostics and in logistics for route optimisation. Some of the most common real-world examples of AI increasing operational efficiency are self-driving cars (Tesla), facial recognition (Apple Face ID), language translation (Google Translate), and medical diagnosis (IBM Watson Health)

Harnessing AI has advantages as it helps optimise the supply chain, extend product life cycles, and ultimately conserve resources and cut operational costs. 

Policy Implications for AI Deployment 

Some of the policy implications for development for AI deployment are as follows:

1. Develop clear and adaptable regulatory frameworks for the ongoing and future developments in AI. The frameworks need to ensure that innovation is not hindered while managing the potential risks. 
2. As AI systems rely on high-quality data that is accessible and interoperable to function effectively and without proper data governance, these systems may produce results that are biased, inaccurate and unreliable. Therefore, it is necessary to ensure data privacy as it is essential to maintain trust and prevent harm to individuals and organisations.
3. Policy developers need to focus on creating policies that upskill the workforce which complements AI development and therefore job displacement. 
4. To ensure cross-border applicability and efficiency of standardising AI policies, the policy-makers need to ensure that international cooperation is achieved when developing the policies.

Addressing Challenges and Risks 

Some of the main challenges that emerge with the development of AI are algorithmic bias, cybersecurity threats and the dependence on exclusive AI solutions or where the company retains exclusive control over the source codes. Some policy approaches that can be taken to mitigate these challenges are:

1. Having a robust accountability mechanism. 
2. Establishing identity and access management policies that have technical controls like authentication and authorisation mechanisms. 
3. Ensure that the learning data that AI systems use follows ethical considerations such as data privacy, fairness in decision-making, transparency, and the interpretability of AI models.

Conclusion 

AI can contribute and provide opportunities to drive operational efficiency in businesses. It can be an optimiser for productivity and costs and foster innovation for different industries. But this power of AI comes with its own considerations and therefore, it must be balanced with proactive policies that address the challenges that emerge such as the need for data governance, algorithmic bias and risks associated with cybersecurity. A solution to overcome these challenges is establishing an adaptable regulatory framework, fostering workforce upskilling and promoting international collaborations. As businesses integrate AI into core functions, it becomes necessary to leverage its potential while safeguarding fairness, transparency, and trust. AI is not just an efficiency tool, it has become a stimulant for organisations operating in a rapidly evolving digital world.

References

• https://indianexpress.com/article/technology/artificial-intelligence/ai-indian-businesses-long-term-gain-operational-efficiency-9717072/ 
• https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html
• https://www.forbes.com/councils/forbestechcouncil/2024/08/06/smart-automation-ais-impact-on-operational-efficiency/ 
• https://www.processexcellencenetwork.com/ai/articles/ai-operational-excellence 
• https://www.leewayhertz.com/ai-for-operational-efficiency/ 
• https://www.forbes.com/councils/forbestechcouncil/2024/11/04/bringing-ai-to-the-enterprise-challenges-and-considerations/  

‍

Executive Summary

‍

Recent claims circulating on social media allege that an Indian Air Force MiG-29 fighter jet was shot down by Pakistani forces during "Operation Sindoor." These reports suggest the incident involved a jet crash attributed to hostile action. However, these assertions have been officially refuted. No credible evidence supports the existence of such an operation or the downing of an Indian aircraft as described. The Indian Air Force has not confirmed any such event, and the claim appears to be misinformation.

Claim

A social media rumor has been circulating, suggesting that an Indian Air Force MiG-29 fighter jet was shot down by Pakistani Air forces during "Operation Sindoor." The claim is accompanied by images purported to show the wreckage of the aircraft.

‍

‍

Fact Check

‍

The social media posts have falsely claimed that a Pakistani Air Force shot down an Indian Air Force MiG-29 during "Operation Sindoor." This claim has been confirmed to be untrue. The image being circulated is not related to any recent IAF operations and has been previously used in unrelated contexts. The content being shared is misleading and does not reflect any verified incident involving the Indian Air Force.

‍

‍

After conducting research by extracting key frames from the video and performing reverse image searches, we successfully traced the original post, which was first published in 2024, and can be seen in a news article from The Hindu and Times of India. 

‍

A MiG-29 fighter jet of the Indian Air Force (IAF), engaged in a routine training mission, crashed near Barmer, Rajasthan, on Monday evening (September 2, 2024). Fortunately, the pilot safely ejected and escaped unscathed, hence the claim is false and an act to spread misinformation.

‍

‍

Conclusion

‍

The claims regarding the downing of an Indian Air Force MiG-29 during "Operation Sindoor" are unfounded and lack any credible verification. The image being circulated is outdated and unrelated to current IAF operations. There has been no official confirmation of such an incident, and the narrative appears to be misleading. Peoples are advised to rely on verified sources for accurate information regarding defence matters.

‍

• Claim: Pakistan Shot down an Indian Fighter Jet, MIG-29
• Claimed On:  Social Media
• Fact Check: False and Misleading 

Introduction

Digital evidence has become part of almost every modern investigation. A photograph can place a person at a location, an audio recording can capture a conversation, and a video can appear to show an event as it happened. For years, the main forensic concern was whether such material had been altered. The rapid growth of generative artificial intelligence has added a harder question: even when a file is preserved exactly as received, can investigators still trust what it appears to show?

Deepfakes have made this question practical rather than theoretical. Synthetic or manipulated audio, video and images can imitate real people and real events with increasing realism. CERT-In describes deepfakes as a high-risk threat because they can support disinformation, fraud, social engineering and reputational harm.[1] NIST research likewise treats AI-generated media as a digital-forensics challenge that requires systematic evaluation of detection technologies.[2]

The result is an evidence problem. The answer is not to stop trusting digital evidence, but to become more disciplined about establishing its origin, integrity, context and authenticity.

The evidence problem begins before the laboratory

When a suspicious video reaches an investigator through WhatsApp, Telegram, email or social media, the file may already have passed through several transformations. It may have been compressed, re-encoded, cropped, renamed or stripped of metadata. A screenshot may preserve what is visible but lose the original file structure. A forwarded audio clip may contain no reliable information about where it was first recorded.

For that reason, forensic examination should begin with acquisition and provenance, not with a quick “deepfake detector” result. Investigators should ask: Who supplied the file? Where was it obtained? Is there an original version? What device or account produced it? What happened to the file before it reached the investigator?

Cryptographic hashing remains important because it can demonstrate that an acquired working copy has not changed during examination. But a valid hash does not prove that the underlying event was genuine. A perfectly preserved fake is still a fake.

What a professional examination should look for

A reliable assessment combines several forms of evidence rather than relying on one technical indicator.

Source and acquisition. The original artefact should be preserved whenever possible. Investigators should record the acquisition method, date and time, source account or device, and any known transformations before collection. A documented chain of custody is essential when material may later support a legal, disciplinary or regulatory decision.

Metadata and file structure. Metadata may provide useful clues about creation, encoding, editing software and timestamps. File structure, compression behaviour and related technical characteristics can also reveal inconsistencies. However, these indicators are supporting evidence, not proof on their own, because metadata can be removed or rewritten during normal processing.

Content-level examination. Forensic analysis can include frame-by-frame video review, audio waveform and spectral examination, and inspection for inconsistencies in lighting, reflections, facial movement, lip synchronisation or background elements. Such signs may help guide an investigation, but they are not a permanent checklist. Generative systems continue to improve.

Independent corroboration. This is often the strongest step. If a recording allegedly shows that a person was in a particular place at a particular time, investigators can compare it with CCTV, access-control records, device artefacts, communications, location information, eyewitness accounts or other independent records. The goal is to determine whether the wider evidence supports the event represented by the media.

A real-world lesson: the Pikesville case

The 2024 Pikesville High School incident in Maryland provides a practical example of why authenticity cannot be assumed from appearance alone. An audio recording circulated online that was presented as the principal making racist and antisemitic comments. On January 17, 2024, Baltimore County Public Schools said it could not yet confirm the recording’s veracity and opened an investigation.[3]

Several months later, the school district reported that investigators, with assistance from the FBI and other experts, had verified that the audio had been created using artificial intelligence.[4] Police subsequently arrested the school’s former athletic director in connection with the fabricated recording.[5]

‍

The forensic lesson is larger than the incident itself. The recording had social consequences before its authenticity was established. In a fast-moving online environment, the first version of an event can travel much further than the later correction. Deepfake investigations therefore have to consider not only whether media is authentic, but also how quickly unverified material can influence decisions.

From deepfake detection to content provenance

Detection tools will remain useful, but they should be treated as part of an examination rather than an automatic verdict. NIST’s Guardians of Forensic Evidence work reflects the need to evaluate how analytic systems perform against changing forms of AI-generated media and how well they generalise beyond controlled conditions.[2]

Another important direction is content provenance. The Coalition for Content Provenance and Authenticity (C2PA) has developed a technical framework for recording verifiable information about how digital content was created and changed. Content Credentials can bind provenance information to an asset using cryptographic techniques, allowing later users to inspect a recorded content history when that information is available.[6]

‍

‍

Provenance does not mean that every claim associated with a file is automatically true. It adds context: who created it, what actions were taken and how the asset changed. In a deepfake environment, that context can be as important as the content itself.

Why this matters in India

The issue is especially relevant to India’s fast-growing digital environment. CERT-In’s 2024 advisory identifies misinformation, fraud and reputational damage among the risks associated with synthetic media.[1] In August 2026, the Government of India stated that the regulatory framework addresses AI-generated deepfakes and noted amendments to the IT Rules in February 2026 concerning harms arising from synthetically generated information, including requirements related to labelling and traceable metadata for permissible AI-generated content.[7]

For organisations, deepfake response should therefore not be treated only as a media or public-relations issue. It can become an incident-response and forensic issue. A suspicious executive voice note, a manipulated employee video or a fabricated screen recording may require preservation, technical examination and independent corroboration before any action is taken.

Conclusion

Deepfakes do not make digital evidence useless. Deepfakes make handling of evidence more dangerous. The professional response is not to believe everything or to doubt everything. The professional response is to build a process around evidence: preserve the original where possible document how evidence was acquired, calculate and record hashes, examine metadata and technical characteristics use detection tools while understanding their limitations compare media with independent evidence and examine provenance information where it is available.

Importantly investigators and decision-makers should separate three questions: Is the file intact? Is the content authentic? Does the content actually prove the event being alleged? Deepfakes can pass the test while failing the other two.

In the age of AI evidence will increasingly be judged not only by how convincing it looks but, by how well its origin, integrity, context and history can be demonstrated. That is the standard that can help preserve trust when seeing and hearing're no longer enough.

References

1. CERT-In, “Deepfakes - Threats and Countermeasures,” Advisory CIAD-2024-0060, 27 November 2024. View source

2. NIST, “Guardians of Forensic Evidence: Evaluating Analytic Systems Against AI-Generated Deepfakes,” 27 January 2025. View source

3. Baltimore County Public Schools, “January 17, 2024, Community Update: Message from Superintendent Dr. Myriam Rogers Regarding Pikesville High School.” View source

4. Baltimore County Public Schools, “April 24, 2024 Staff and Community Update: Message from Superintendent Dr. Myriam Rogers Regarding Pikesville High School Investigation.” View source

5. The Baltimore Banner / WYPR, “Ex-athletic director framed principal with AI-generated voice, police say,” 25 April 2024. View source

6. Coalition for Content Provenance and Authenticity (C2PA), “Content Credentials: C2PA Technical Specification,” Version 2.1. View source

7. Government of India, Ministry of Electronics & Information Technology, “Government Strengthens Regulatory Framework to Address AI-Generated Deepfakes,” 6 August 2026. View source

‍