#FactCheck-AI-Generated Video Falsely Shows Samay Raina Making a Joke on Rekha

Introduction

In the digital landscape, there is a rapid advancement of technologies such as generative AI(Artificial Intelligence), deepfake, machine learning, etc. Such technologies offer convenience to users in performing several tasks and are capable of assisting individuals and business entities. Certain regulatory mechanisms are also established for the ethical and reasonable use of such advanced technologies. However, these technologies are easily accessible; hence, cyber-criminals leverage AI tools and technologies for malicious activities or for committing various cyber frauds. By such misuse of advanced technologies, new cyber threats have emerged. 

Deepfake Scams 

Deepfake is an AI-based technology. Deepfake is capable of creating realistic images or videos which in actuality are created by machine algorithms. Deepfake technology, since easily accessible, is misused by fraudsters to commit various cyber crimes or deceive and scam people through fake images or videos that look realistic. By using the Deepfake technology, cybercriminals manipulate audio and video content, which looks very realistic but, in actuality, is fake. 

Voice cloning 

To create a voice clone of anyone's, audio can be deepfaked too, which closely resembles a real one but, in actuality, is a fake voice created through deepfake technology. Recently, in Kerala, a man fell victim to an AI-based video call on WhatsApp. He received a video call from a person claiming to be his former colleague. The scammer, using AI deepfake technology, impersonated the face of his former colleague and asked for financial help of 40,000. 

Uttarakhand Police issues warning admitting the rising trend of AI-based scams

Recently, Uttarakhand police’s Special Task Force (STF) has issued a warning admitting the widespread of AI technology-based scams such as deepfake or voice cloning scams targeting innocent people. Police expressed concern that several incidents have been reported where innocent people are lured by cybercriminals. Cybercriminals exploit advanced technologies and manipulate innocent people to believe that they are talking to their close ones or friends, but in actuality, they are fake voice clones or deepfake video calls. In this way, cybercriminals ask for immediate financial help, which ultimately leads to financial losses for victims of such scams. 

Tamil Nadu Police Issues advisory on deepfake scams

To deceive people, cyber criminals misuse deepfake technologies and target them for financial gain. Recently, Tamilnadu Police Cyberwing have issued an advisory on rising deepfake scams. Fraudsters are creating highly convincing images, videos or voice clones to defraud innocent people and make them victims of financial fraud. The advisory states that you limit the personal data you share you share online and adjust privacy settings. Advisory says to promptly report any suspicious activity or cyber crimes to 1930 or the National Cyber Crime Reporting portal.

Best practices 

1. Pay attention if you notice compromised video quality because deepfake videos often have compromised or poor video quality and unusual blur resolution, which poses a question to its genuineness. Deepfake videos often loop or unusually freeze, which indicates that the video content might be fabricated.

1. Whenever you receive requests for any immediate financial help, act responsively and verify the situation by directly contacting the person on his primary contact number.
2. You need to be vigilant and cautious, since scammers often possess a sense of urgency, leading to giving no time for the victim to think about it and deceiving them by making a quick decision. Scammers pose sudden emergencies and demand financial support on an urgent basis. 
3. Be aware of the recent scams and follow the best practices to stay protected from rising cyber frauds. 
4. Verify the identity of unknown callers.
5. Utilise privacy settings on your social media.
6. Pay attention if you notice any suspicious nature, and avoid sharing voice notes with unknown users because scammers might use them as voice samples and create your voice clone. 
7. If you fall victim to such frauds, one powerful resource available is the National Cyber Crime Reporting Portal (www.cybercrime.gov.in) and the 1930 toll-free helpline number where you can report cyber fraud, including any financial crimes.

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Conclusion

AI-powered technologies are leveraged by cybercriminals to commit cyber crimes such as deepfake scams, voice clone scams, etc. Where innocent people are lured by scammers. Hence there is a need for awareness and caution among the people.  We should be vigilant and aware of the growing incidents of AI-based cyber scams. Must follow the best practices to stay protected.

References: 

• https://www.the420.in/ai-voice-cloning-cyber-crime-alert-uttarakhand-police/
• https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/exploiting-ai-how-cybercriminals-misuse-abuse-ai-and-ml#:~:text=AI%20and%20ML%20Misuses%20and%20Abuses%20in%20the%20Future&text=Through%20the%20use%20of%20AI,and%20business%20processes%20are%20compromised.
• https://www.ndtv.com/india-news/kerala-man-loses-rs-40-000-to-ai-based-deepfake-scam-heres-what-it-is-4217841
• https://news.bharattimes.co.in/t-n-cybercrime-police-issue-advisory-on-deepfake-scams/

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Executive Summary
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As Kerala gears up for its upcoming Assembly elections, political activity has intensified across the state. Amid this charged atmosphere, a purported image of Narendra Modi has gone viral on social media. The image shows the Prime Minister holding coconuts, with several photographers around him. It is being claimed that the photo was taken during his recent visit for the 2026 Kerala Assembly election campaign.

However, an research by the CyberPeace has debunked the claim, confirming that the image is fake and generated using artificial intelligence.

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Claim

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A Facebook user named “Javed Ahmad” shared the viral image on April 6, 2026, with a sarcastic caption suggesting that the Prime Minister was posing for reels during his Kerala visit.

• https://www.facebook.com/Ahmadspeaks4u/posts/pfbid0aRnhwXBjnfSXF9S48vaH1gTQrGQmxnRmhpJCRxMWazqLJhYs6zxM9rot51qtBgRLl 
• https://perma.cc/N3AM-Q2YC 

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Fact Check

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To verify the claim, we conducted keyword searches on Google but found no credible news reports supporting it. A reverse image search also did not yield any reliable sources. Additionally, a thorough review of the Prime Minister’s official social media accounts showed no trace of such an image or event. Given these inconsistencies, suspicion arose about the image’s authenticity.

The viral image was then analyzed using AI detection tools. Results from HIVE Moderation indicated a 99% probability that the image is AI-generated.
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Another tool, Wasit AI, also confirmed that the image is artificially created.

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Conclusion

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The claim is false. The viral image of Prime Minister Narendra Modi is not real and has been generated using AI tools, likely similar to Google AI.

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Executive Summary:

In late 2024 an Indian healthcare provider experienced a severe cybersecurity attack that demonstrated how powerful AI ransomware is. This blog discusses the background to the attack, how it took place and the effects it caused (both medical and financial), how organisations reacted, and the final result of it all, stressing on possible dangers in the healthcare industry with a lack of sufficiently adequate cybersecurity measures in place. The incident also interrupted the normal functioning of business and explained the possible economic and image losses from cyber threats. Other technical results of the study also provide more evidence and analysis of the advanced AI malware and best practices for defending against them. 

1. Introduction

The integration of artificial intelligence (AI) in cybersecurity has revolutionised both defence mechanisms and the strategies employed by cybercriminals. AI-powered attacks, particularly ransomware, have become increasingly sophisticated, posing significant threats to various sectors, including healthcare. This report delves into a case study of an AI-powered ransomware attack on a prominent Indian healthcare provider in 2024, analysing the attack's execution, impact, and the subsequent response, along with key technical findings.

2. Background 

 In late 2024, a leading healthcare organisation in India which is involved in the research and development of AI techniques fell prey to a ransomware attack that was AI driven to get the most out of it. With many businesses today relying on data especially in the healthcare industry that requires real-time operations, health care has become the favourite of cyber criminals. AI aided attackers were able to cause far more detailed and damaging attack that severely affected the operation of the provider whilst jeopardising the safety of the patient information.  

 3. Attack Execution 

The attack began with the launch of a phishing email designed to target a hospital administrator. They received an email with an infected attachment which when clicked in some cases injected the AI enabled ransomware into the hospitals network. AI incorporated ransomware was not as blasé as traditional ransomware, which sends copies to anyone, this studied the hospital’s IT network. First, it focused and targeted important systems which involved implementation of encryption such as the electronic health records and the billing departments. 

The fact that the malware had an AI feature allowed it to learn and adjust its way of propagation in the network, and prioritise the encryption of most valuable data. This accuracy did not only increase the possibility of the potential ransom demand but also it allowed reducing the risks of the possibility of early discovery. 

 4. Impact 

•  The consequences of the attack were immediate and severe: The consequences of the attack were immediate and severe.
•  Operational Disruption: The centralization of important systems made the hospital cease its functionality through the acts of encrypting the respective components. Operations such as surgeries, routine medical procedures and admitting of patients were slowed or in some cases referred to other hospitals. 
•  Data Security: Electronic patient records and associated billing data became off-limit because of the vulnerability of patient confidentiality. The danger of data loss was on the verge of becoming permanent, much to the concern of both the healthcare provider and its patients. 
•  Financial Loss: The attackers asked for 100 crore Indian rupees (approximately 12 USD million) for the decryption key. Despite the hospital not paying for it, there were certain losses that include the operational loss due to the server being down, loss incurred by the patients who were affected in one way or the other, loss incurred in responding to such an incident and the loss due to bad reputation. 

5. Response

As soon as the hotel’s management was informed about the presence of ransomware, its IT department joined forces with cybersecurity professionals and local police. The team decided not to pay the ransom and instead recover the systems from backup. Despite the fact that this was an ethically and strategically correct decision, it was not without some challenges. Reconstruction was gradual, and certain elements of the patients’ records were permanently erased. 

 In order to avoid such attacks in the future, the healthcare provider put into force several organisational and technical actions such as network isolation and increase of cybersecurity measures. Even so, the attack revealed serious breaches in the provider’s IT systems security measures and protocols. 

6. Outcome

The attack had far-reaching consequences:

• Financial Impact: A healthcare provider suffers a lot of crashes in its reckoning due to substantial service disruption as well as bolstering cybersecurity and compensating patients. 
•  Reputational Damage: The leakage of the data had a potential of causing a complete loss of confidence from patients and the public this affecting the reputation of the provider. This, of course, had an effect on patient care, and ultimately resulted in long-term effects on revenue as patients were retained. 
•  Industry Awareness: The breakthrough fed discussions across the country on how to improve cybersecurity provisions in the healthcare industry. It woke up the other care providers to review and improve their cyber defence status. 

7. Technical Findings

The AI-powered ransomware attack on the healthcare provider revealed several technical vulnerabilities and provided insights into the sophisticated mechanisms employed by the attackers. These findings highlight the evolving threat landscape and the importance of advanced cybersecurity measures.

7.1 Phishing Vector and Initial Penetration

• Sophisticated Phishing Tactics: The phishing email was crafted with precision, utilising AI to mimic the communication style of trusted contacts within the organisation. The email bypassed standard email filters, indicating a high level of customization and adaptation, likely due to AI-driven analysis of previous successful phishing attempts.
• Exploitation of Human Error: The phishing email targeted an administrative user with access to critical systems, exploiting the lack of stringent access controls and user awareness. The successful penetration into the network highlighted the need for multi-factor authentication (MFA) and continuous training on identifying phishing attempts.

7.2 AI-Driven Malware Behavior

• Dynamic Network Mapping: Once inside the network, the AI-powered malware executed a sophisticated mapping of the hospital's IT infrastructure. Using machine learning algorithms, the malware identified the most critical systems—such as Electronic Health Records (EHR) and the billing system—prioritising them for encryption. This dynamic mapping capability allowed the malware to maximise damage while minimising its footprint, delaying detection.
• Adaptive Encryption Techniques: The malware employed adaptive encryption techniques, adjusting its encryption strategy based on the system's response. For instance, if it detected attempts to isolate the network or initiate backup protocols, it accelerated the encryption process or targeted backup systems directly, demonstrating an ability to anticipate and counteract defensive measures.
• Evasive Tactics: The ransomware utilised advanced evasion tactics, such as polymorphic code and anti-forensic features, to avoid detection by traditional antivirus software and security monitoring tools. The AI component allowed the malware to alter its code and behaviour in real time, making signature-based detection methods ineffective.

7.3 Vulnerability Exploitation

• Weaknesses in Network Segmentation: The hospital’s network was insufficiently segmented, allowing the ransomware to spread rapidly across various departments. The malware exploited this lack of segmentation to access critical systems that should have been isolated from each other, indicating the need for stronger network architecture and micro-segmentation.
• Inadequate Patch Management: The attackers exploited unpatched vulnerabilities in the hospital’s IT infrastructure, particularly within outdated software used for managing patient records and billing. The failure to apply timely patches allowed the ransomware to penetrate and escalate privileges within the network, underlining the importance of rigorous patch management policies.

7.4 Data Recovery and Backup Failures

• Inaccessible Backups: The malware specifically targeted backup servers, encrypting them alongside primary systems. This revealed weaknesses in the backup strategy, including the lack of offline or immutable backups that could have been used for recovery. The healthcare provider’s reliance on connected backups left them vulnerable to such targeted attacks.
• Slow Recovery Process: The restoration of systems from backups was hindered by the sheer volume of encrypted data and the complexity of the hospital’s IT environment. The investigation found that the backups were not regularly tested for integrity and completeness, resulting in partial data loss and extended downtime during recovery.

7.5 Incident Response and Containment

• Delayed Detection and Response: The initial response was delayed due to the sophisticated nature of the attack, with traditional security measures failing to identify the ransomware until significant damage had occurred. The AI-powered malware’s ability to adapt and camouflage its activities contributed to this delay, highlighting the need for AI-enhanced detection and response tools.
• Forensic Analysis Challenges: The anti-forensic capabilities of the malware, including log wiping and data obfuscation, complicated the post-incident forensic analysis. Investigators had to rely on advanced techniques, such as memory forensics and machine learning-based anomaly detection, to trace the malware’s activities and identify the attack vector.

8. Recommendations Based on Technical Findings

To prevent similar incidents, the following measures are recommended:

1. AI-Powered Threat Detection: Implement AI-driven threat detection systems capable of identifying and responding to AI-powered attacks in real time. These systems should include behavioural analysis, anomaly detection, and machine learning models trained on diverse datasets.
2. Enhanced Backup Strategies: Develop a more resilient backup strategy that includes offline, air-gapped, or immutable backups. Regularly test backup systems to ensure they can be restored quickly and effectively in the event of a ransomware attack.
3. Strengthened Network Segmentation: Re-architect the network with robust segmentation and micro-segmentation to limit the spread of malware. Critical systems should be isolated, and access should be tightly controlled and monitored.
4. Regular Vulnerability Assessments: Conduct frequent vulnerability assessments and patch management audits to ensure all systems are up to date. Implement automated patch management tools where possible to reduce the window of exposure to known vulnerabilities.
5. Advanced Phishing Defences: Deploy AI-powered anti-phishing tools that can detect and block sophisticated phishing attempts. Train staff regularly on the latest phishing tactics, including how to recognize AI-generated phishing emails.

9. Conclusion

The AI empowered ransomware attack on the Indian healthcare provider in 2024 makes it clear that the threat of advanced cyber attacks has grown in the healthcare facilities. Sophisticated technical brief outlines the steps used by hackers hence underlining the importance of ongoing active and strong security. This event is a stark message to all about the importance of not only remaining alert and implementing strong investments in cybersecurity but also embarking on the formulation of measures on how best to counter such incidents with limited harm. AI is now being used by cybercriminals to increase the effectiveness of the attacks they make and it is now high time all healthcare organisations ensure that their crucial systems and data are well protected from such attacks.

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