#FactCheck: Viral Video Claiming IAF Air Chief Marshal Acknowledged Loss of Jets Found Manipulated

Introduction

Deepfakes are artificial intelligence (AI) technology that employs deep learning to generate realistic-looking but phoney films or images. Algorithms use large volumes of data to analyse and discover patterns in order to provide compelling and realistic results. Deepfakes use this technology to modify movies or photos to make them appear as if they involve events or persons that never happened or existed.The procedure begins with gathering large volumes of visual and auditory data about the target individual, which is usually obtained from publicly accessible sources such as social media or public appearances. This data is then utilised for training a deep-learning model to resemble the target of deep fakes.

Recent Cases of Deepfakes-‍

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In an unusual turn of events, a man from northern China became the victim of a sophisticated deep fake technology. This incident has heightened concerns about using artificial intelligence (AI) tools to aid financial crimes, putting authorities and the general public on high alert.
During a video conversation, a scammer successfully impersonated the victim’s close friend using AI-powered face-swapping technology. The scammer duped the unwary victim into transferring 4.3 million yuan (nearly Rs 5 crore). The fraud occurred in Baotou, China.

AI ‘deep fakes’ of innocent images fuel spike in sextortion scams
Artificial intelligence-generated “deepfakes” are fuelling sextortion frauds like a dry brush in a raging wildfire. According to the FBI, the number of nationally reported sextortion instances came to 322% between February 2022 and February 2023, with a notable spike since April due to AI-doctored photographs. And as per the FBI, innocent photographs or videos posted on social media or sent in communications can be distorted into sexually explicit, AI-generated visuals that are “true-to-life” and practically hard to distinguish. According to the FBI, predators often located in other countries use doctored AI photographs against juveniles to compel money from them or their families or to obtain actual sexually graphic images.

Deepfake Applications

• Lensa AI.
• Deepfakes Web.
• Reface.
• MyHeritage.
• DeepFaceLab.
• Deep Art.
• Face Swap Live.
• FaceApp.

Deepfake examples

There are numerous high-profile Deepfake examples available. Deepfake films include one released by actor Jordan Peele, who used actual footage of Barack Obama and his own imitation of Obama to convey a warning about Deepfake videos.
A video shows Facebook CEO Mark Zuckerberg discussing how Facebook ‘controls the future’ with stolen user data, most notably on Instagram. The original video is from a speech he delivered on Russian election meddling; only 21 seconds of that address were used to create the new version. However, the vocal impersonation fell short of Jordan Peele’s Obama and revealed the truth.

The dark side of AI-Generated Misinformation

• Misinformation generated by AI-generated the truth, making it difficult to distinguish fact from fiction.
• People can unmask AI content by looking for discrepancies and lacking the human touch.
• AI content detection technologies can detect and neutralise disinformation, preventing it from spreading.

Safeguards against Deepfakes-

Technology is not the only way to guard against Deepfake videos. Good fundamental security methods are incredibly effective for combating Deepfake.For example, incorporating automatic checks into any mechanism for disbursing payments might have prevented numerous Deepfake and related frauds. You might also:

• Regular backups safeguard your data from ransomware and allow you to restore damaged data.
• Using different, strong passwords for different accounts ensures that just because one network or service has been compromised, it does not imply that others have been compromised as well. You do not want someone to be able to access your other accounts if they get into your Facebook account.
• To secure your home network, laptop, and smartphone against cyber dangers, use a good security package such as Kaspersky Total Security. This bundle includes anti-virus software, a VPN to prevent compromised Wi-Fi connections, and webcam security.

What is the future of Deepfake –

Deepfake is constantly growing. Deepfake films were easy to spot two years ago because of the clumsy movement and the fact that the simulated figure never looked to blink. However, the most recent generation of bogus videos has evolved and adapted.
There are currently approximately 15,000 Deepfake videos available online. Some are just for fun, while others attempt to sway your opinion. But now that it only takes a day or two to make a new Deepfake, that number could rise rapidly.

Conclusion-

The distinction between authentic and fake content will undoubtedly become more challenging to identify as technology advances. As a result, experts feel it should not be up to individuals to discover deep fakes in the wild. “The responsibility should be on the developers, toolmakers, and tech companies to create invisible watermarks and signal what the source of that image is,” they stated. Several startups are also working on approaches for detecting deep fakes.

As AI language models become more powerful, they are also becoming more prone to errors. One increasingly prominent issue is AI hallucinations, instances where models generate outputs that are factually incorrect, nonsensical, or entirely fabricated, yet present them with complete confidence. Recently, ChatGPT released two new models—o3 and o4-mini, which differ from earlier versions as they focus more on step-by-step reasoning rather than simple text prediction. With the growing reliance on chatbots and generative models for everything from news summaries to legal advice, this phenomenon poses a serious threat to public trust, information accuracy, and decision-making.

What Are AI Hallucinations?

AI hallucinations occur when a model invents facts, misattributes quotes, or cites nonexistent sources. This is not a bug but a side effect of how Large Language Models (LLMs) work, and it is only the probability that can be reduced, not their occurrence altogether. Trained on vast internet data, these models predict what word is likely to come next in a sequence. They have no true understanding of the world or facts, they simulate reasoning based on statistical patterns in text. What is alarming is that the newer and more advanced models are producing more hallucinations, not fewer. seemingly counterintuitive. This has been prevalent reasoning-based models, which generate answers step-by-step in a chain-of-thought style. While this can improve performance on complex tasks, it also opens more room for errors at each step, especially when no factual retrieval or grounding is involved.

As per reports shared on TechCrunch, it mentioned that when users asked AI models for short answers, hallucinations increased by up to 30%. And a study published in eWeek found that ChatGPT hallucinated in 40% of tests involving domain-specific queries, such as medical and legal questions. This was not, however, limited to this particular Large Language Model, but also similar ones like DeepSeek. Even more concerning are hallucinations in multimodal models like those used for deepfakes. Forbes reports that some of these models produce synthetic media that not only look real but are also capable of contributing to fabricated narratives, raising the stakes for the spread of misinformation during elections, crises, and other instances.

It is also notable that AI models are continually improving with each version, focusing on reducing hallucinations and enhancing accuracy. New features, such as providing source links and citations, are being implemented to increase transparency and reliability in responses.

The Misinformation Dilemma

The rise of AI-generated hallucinations exacerbates the already severe problem of online misinformation. Hallucinated content can quickly spread across social platforms, get scraped into training datasets, and re-emerge in new generations of models, creating a dangerous feedback loop. However, it helps that the developers are already aware of such instances and are actively charting out ways in which we can reduce the probability of this error. Some of them are:

1. Retrieval-Augmented Generation (RAG): Instead of relying purely on a model’s internal knowledge, RAG allows the model to “look up” information from external databases or trusted sources during the generation process. This can significantly reduce hallucination rates by anchoring responses in verifiable data.
2. Use of smaller, more specialised language models: Lightweight models fine-tuned on specific domains, such as medical records or legal texts. They tend to hallucinate less because their scope is limited and better curated.

Furthermore, transparency mechanisms such as source citation, model disclaimers, and user feedback loops can help mitigate the impact of hallucinations. For instance, when a model generates a response, linking back to its source allows users to verify the claims made.

Conclusion

AI hallucinations are an intrinsic part of how generative models function today, and such a side-effect would continue to occur until foundational changes are made in how models are trained and deployed. For the time being, developers, companies, and users must approach AI-generated content with caution. LLMs are, fundamentally, word predictors, brilliant but fallible. Recognising their limitations is the first step in navigating the misinformation dilemma they pose.

References

• https://www.eweek.com/news/ai-hallucinations-increase/
• https://www.resilience.org/stories/2025-05-11/better-ai-has-more-hallucinations/ 
• https://www.ekathimerini.com/nytimes/1269076/ai-is-getting-more-powerful-but-its-hallucinations-are-getting-worse/ 
• https://techcrunch.com/2025/05/08/asking-chatbots-for-short-answers-can-increase-hallucinations-study-finds/ 
• https://en.as.com/latest_news/is-chatgpt-having-robot-dreams-ai-is-hallucinating-and-producing-incorrect-information-and-experts-dont-know-why-n/
• https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay/
• https://www.forbes.com/sites/conormurray/2025/05/06/why-ai-hallucinations-are-worse-than-ever/ 
• https://towardsdatascience.com/how-i-deal-with-hallucinations-at-an-ai-startup-9fc4121295cc/ 
• https://www.informationweek.com/machine-learning-ai/getting-a-handle-on-ai-hallucinations 

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Introduction
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With the modernization of automobiles, so have the methods employed by criminals who seek to commit thefts. The old method of smashing a car window or bypassing an engine lock is no longer prevalent. Modern car thieves employ cloning techniques for keys and digital signals, and sophisticated methods to commit crimes without any traces left behind. In an era where intelligence is crucial, the forensic examination of car keys has become an indispensable tool for investigations, providing clues buried within ordinary car keys.
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The Need for Car Key Forensics Today

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Daily, thousands of cars worth millions are being hacked or stolen around the globe. The shocking thing is that most of these hacks do not have any sign of breakage or forced entry. It is because the thieves use vulnerabilities in the wireless key systems to unlock the vehicles without leaving any trace behind. Therefore, car-key forensics has now become more important than ever before.

Forensic Value of Smart Key

The smart key is not just comprised of locking and unlocking features for vehicles. Actually, it operates as a miniature computer within the key itself. Information such as pairing records, frequency of use, or the last instance that the key was used to unlock something could be contained in smart keys, thus offering evidence of any criminal activity conducted using these items.

Patterns of Vehicle Theft

Through the examination of the chip in a key, one will be able to establish whether the key was legally programmed or had been tampered with by some other means. Such forensics become more important when trying to detect and monitor any car theft rings, which employ cloning machines or software.

Confirming Ownership and Authenticity

In cases involving insurance claims or fraud, smart key data can help confirm whether the person making a claim actually owned or used the car during the incident. It’s digital proof that goes beyond what paperwork or statements can show.

Strengthening Legal Cases

When brought to court, data doesn’t lie. A properly handled forensic examination of a car key can provide hard evidence — the kind that holds up under questioning and supports or disproves claims with complete accuracy. In many cases, this small device becomes the most reliable witness in the investigation.

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A Real-Life Example
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Consider the following scenario: An expensive SUV is stolen from a parking lot secured with security surveillance. No one is captured on camera and there is no sign of forced entry. After days of investigation, the police end up arresting a suspect with a smart key.

During forensic analysis of the smart key, it is revealed that:

• The transponder has an ID number which can be matched against the immobilizer installed in the vehicle.
• The rolling code counter has been incremented in such a way that the date corresponds with the report of theft of the vehicle.
• The extracted information helps match the pair timestamp of the key with the particular make and model of the vehicle.

All this from a single piece of evidence – the smart key.

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Inside a Smart Key: Where the Data Lives

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An average smart key is not only a remote but also a multi-level set of data carriers:

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• Plastic Shell– could include serial numbers and information on the manufacture.
• Battery – helps calculate the time of using the key or detect any modifications.
• Antenna Coil – sends encrypted information to the immobilizer of a car. Draw the picture of this element.
• Microchip / EEPROM – holds key identification code, rolling code, VIN number, and/or other information of the vehicle.
• Buttons / sensors – could record any pressing or transmission actions in some cases.

All the little devices above, once properly studied via forensics software, provide valuable information.

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How the Investigator Uncovers the Truth
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In relation to investigating the true story about a vehicle, it is no different than handling other forms of digital evidence, as forensic analysts treat the car key in the same way. It is nothing more than an encrypted device in your hands, and using special techniques, they are able to reveal the information contained within the device.

Some of the methods used by modern forensic laboratories include:

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1. Intercepting Radio Signals

Any intelligent key transmits radio signals to communicate with a car. Specialists employ advanced antennas and radio frequency (RF) analysers to catch and analyse them. This way, it is possible to understand the interaction between the key and the car – how often was it used, what kind of authentication procedure takes place, and if the signal matches the car’s one or has been forged somehow.

2. Checking Out the Key’s Brain (Analysis of EEPROM)

There is always a special chip on the key that is responsible for its activity. The chip contains an important memory module (EEPROM – Electrically Erasable Programmable Read-Only Memory), which holds various data, including key IDs and rolling codes. It can be carefully retrieved via advanced tools. Thus, it is possible to determine whether somebody tried to tamper with the key.

3. The Correlation between the Key and Car’s Information

The information stored inside the key will not be used separately since investigators will correlate the key's data with that of the vehicle itself (ECU and immobilizer). If the two kinds of information coincide, the investigation may conclude that the key belongs to the vehicle. Otherwise, this may mean either cloning or tampering.

 4. Identifying the Tampering and Cloning Evidence

As was mentioned above, thieves sometimes resort to using unlawful programming devices for duplicating smart car keys. In order to detect possible cloning, experts examine the key using various diagnostic devices to find out whether the keys were modified by changing the encryption code, frequencies, and hardware itself.

At the end of the process, some kind of miracle occurs because of the following: all actions committed with this particular key become documented, recorded inside the device itself. Even if someone tries to hide anything or remove any information concerning this particular incident, there will always remain some data.

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Car Key Forensics in the Future
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The evolution of cars to connect with other devices and adopt self-driving technologies requires new investigative methods to be used for vehicle-related crimes. Advanced car keys or smartphone apps that replace physical keys will likely incorporate biometric authentication, cloud integration, or blockchain records of key activity in the near future.

Such improvements will pose several threats and offer many benefits:

• Artificial intelligence tools can determine if the car key is cloned based on its behaviour pattern.
• Blockchain validation ensures all key-related activities are recorded and cannot be altered.
• Cyber-forensic protocols will become increasingly necessary for investigating criminal activity related to vehicles.

Car key forensics technology will not only allow solving crimes but may become instrumental in crime prevention.

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Conclusion
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A car key in this era is more than just an unlocking mechanism; it is a miniature data storage facility, which can yield information about the user, intentions, and access rights. The more cars become technologically advanced, the more the examination of smart keys becomes necessary as part of correlating physical evidence with digital investigation. It clearly indicates how small objects such as keys can play pivotal roles in cracking cases.

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