Scams, Spams and Shams Revealed | Why Do So Many UK Celebrity Deepfake Adverts Appear on Social Media?

Several factors contribute to the proliferation of UK celebrity deepfake adverts on social media platforms:

Technological advancements: Deepfake technology has become increasingly sophisticated and accessible, making it easier for individuals or groups with malicious intent to create convincing fake videos.
Lack of regulation: The legal framework surrounding deepfakes in advertising is still evolving. This lack of clear regulation leaves room for exploitation and misuse of the technology.
Profit motive: Deepfake ads can be highly effective in capturing attention and driving engagement. Unscrupulous advertisers may use deepfakes of celebrities to promote products or services without their consent, aiming for financial gain.
Viral potential: Deepfake videos have the potential to go viral on social media, providing a wider reach for fraudulent advertisers and increasing the potential for scams.
Limited platform control: Although social media platforms are working to detect and remove deepfakes, the sheer volume of content makes it difficult to control completely.

Specific to the UK:

High concentration of celebrities: The UK has a large pool of well-known celebrities, making them attractive targets for deepfake creators seeking to capitalize on their fame and influence.
Strong advertising industry: The UK has a thriving advertising industry, which may make it a more attractive market for fraudulent advertisers looking to exploit deepfake technology.

The combination of these factors creates a fertile ground for the spread of UK celebrity deepfake adverts on social media platforms. It's a growing concern that demands attention from both regulatory bodies and social media companies to safeguard consumers and protect the reputations of public figures.

YouTube Can Spot a Song or Video Even in the Background

Yes, YouTube has sophisticated Content ID technology that allows it to identify copyrighted songs and videos even if they are playing in the background of another video. Here's how it works:

Fingerprinting: YouTube creates a unique audio and video fingerprint for every copyrighted piece of content in its database.
Scanning: When a new video is uploaded, Content ID scans the audio and video tracks and compares them to the fingerprints in its database.
Matching: If a match is found, Content ID can automatically take actions based on the copyright holder's preferences, such as:
- Monetization: Running ads on the video and sharing revenue with the copyright holder.
- Tracking: Tracking the video's viewership statistics.
- Blocking: Preventing the video from being viewed in certain countries or entirely.
- Muting: Muting the copyrighted audio in the video.

This technology is incredibly powerful and accurate, and it's designed to protect the rights of copyright holders. However, it's important to note that Content ID is not perfect, and there can be false positives or disputes over ownership.

If you're a creator, it's essential to be aware of Content ID and use only music and video that you have the rights to use. If you're unsure, there are many resources available to help you find royalty-free or Creative Commons-licensed content.

So Why Can't Facebook Use Face Recognition?

While face recognition technology has advanced significantly, using it to spot deepfakes on platforms like Facebook presents a complex challenge due to several reasons:

Evolving Deepfake Technology: Deepfake creation tools are constantly improving, making them more sophisticated and harder to detect. Face recognition systems may struggle to keep up with the rapid advancements in deepfake generation techniques.
Subtle Manipulation: Deepfakes can involve subtle manipulations of facial expressions, movements, and even voice, making them difficult to distinguish from real videos using solely face recognition.
Privacy Concerns: Implementing widespread face recognition on social media raises significant privacy concerns. Users may not be comfortable with their faces being constantly scanned and analyzed.
Scalability: Facebook handles a massive volume of video content, making it computationally expensive and time-consuming to run face recognition analysis on every video uploaded.
False Positives and Negatives: Face recognition systems are not infallible. They can produce false positives (flagging real videos as deepfakes) or false negatives (failing to detect actual deepfakes).
Adversarial Attacks: Deepfake creators may actively try to fool face recognition systems by using specific techniques to bypass detection.

While face recognition can be a helpful tool in identifying some deepfakes, it's not a foolproof solution. Facebook and other social media platforms are exploring a combination of approaches, including AI-based detection, user reporting, and third-party verification, to combat the spread of deepfakes. However, the fight against deepfakes is an ongoing challenge, and it will require a multi-faceted approach to effectively address this issue.

Why Can't Social Media Detect the Obvious Fake Text Like:

'UK Banks are Unhappy With This New Trick'

While the phrase "UK Banks are Unhappy With This New Trick" might seem like an obvious clickbait headline to us, detecting it automatically on social media poses several challenges:

Contextual Understanding: While the phrase itself is generic and suspicious, understanding its context requires more advanced natural language processing (NLP) capabilities. Social media algorithms might struggle to discern if the surrounding text is genuinely informative or part of a scam.
Evolving Language: Clickbait phrases and scam tactics are constantly evolving. What seems obvious today might be subtly modified tomorrow, making it harder for static detection systems to keep up.
Subjective Interpretation: What constitutes "obvious fake text" can be subjective. While some users might immediately recognize the manipulative nature of the phrase, others might not, making it difficult to set a universal threshold for detection.
Free Speech Concerns: Social media platforms also need to balance their efforts to combat scams with protecting freedom of expression. Blocking content based solely on specific phrases might lead to censorship of legitimate content.
Scale and Efficiency: Social media platforms process massive amounts of content. Implementing complex NLP analysis on every post and ad can be computationally intensive and impact performance.

However, social media platforms are making strides in combating such scams using a combination of approaches:

Machine Learning and AI: Advanced algorithms are being trained to identify patterns in language, user behavior, and link sharing associated with scams.
User Reporting: Platforms rely on users to report suspicious content, which helps train their detection systems.
Third-Party Verification: Fact-checking organizations and other partners can flag misleading content.
Educational Campaigns: Platforms often run awareness campaigns to educate users about recognizing scams and protecting themselves.

While the fight against fake text and scams is ongoing, the combination of these approaches aims to make social media safer for everyone.

Stop Being so Greedy and Get a Human to Check

There are several reasons why Facebook might not have humans check every advert text, despite the clear benefits of doing so:

Scale and Cost: Facebook processes an enormous volume of ads. Employing enough humans to manually review every single one would be extremely expensive and logistically challenging.
Efficiency: Manual review is inherently slower than automated systems. Relying solely on humans could significantly delay ad approvals and impact the overall efficiency of Facebook's advertising platform.
Subjectivity: Determining what constitutes "fake" or "misleading" text can be subjective. Human reviewers might have varying interpretations, leading to inconsistencies in ad approvals.
Automation Bias: While humans can catch nuances that machines might miss, they can also be prone to biases and errors. Relying solely on human judgment might not necessarily guarantee a foolproof solution.
Profit Motive: Facebook's primary revenue source is advertising. Implementing stricter ad review processes, even if beneficial for users, might reduce ad volume and impact their profits.

However, there are strong arguments for why Facebook should prioritize human review, especially for sensitive categories or high-risk content:

User Safety: Protecting users from scams, misinformation, and harmful content should be a top priority, even if it comes at a financial cost.
Brand Reputation: Repeatedly allowing misleading ads to slip through damages Facebook's reputation and erodes user trust.
Regulatory Pressure: Increasing calls for greater platform accountability might force Facebook to invest more in human moderation to avoid potential fines or restrictions.
Ethical Considerations: As a powerful platform with significant societal influence, Facebook has a moral obligation to ensure its advertising practices are responsible and ethical.

In conclusion, while the challenges of implementing human review are understandable, Facebook's responsibility towards its users and the broader society should outweigh purely financial considerations. A balanced approach that combines automated systems with strategic human oversight, especially for sensitive content, is likely the most effective way forward.

Just an image as ridiculous as this could surely be blocked:

Yes, an image like this could and should be blocked by Facebook. It displays several classic hallmarks of a potential scam or misleading advertisement:

Unrealistic Claims: "Eliminated all guesswork in trading" is a bold and improbable statement. Successful trading inherently involves some level of risk and uncertainty.
"Secret Tactic" Hype: The use of phrases like "secret tactic" creates a sense of exclusivity and FOMO (fear of missing out), often used to lure people into questionable schemes.
Lack of Transparency: The image offers no concrete information about the supposed tactic or the company itself. It's all hype and no substance.
Emotional Manipulation: The pointing finger emoji adds a layer of pressure, urging viewers to take immediate action without providing any real information to base a decision on.

Such images can be particularly harmful because they prey on people's desire for quick financial gains and exploit their trust. They can lead to significant financial losses and emotional distress for those who fall victim to the scams they often promote.

Facebook's advertising policies explicitly prohibit misleading or deceptive content. While their automated systems might not always catch such nuanced cases, the image certainly warrants closer scrutiny and potential removal. Users should also be encouraged to report such ads to help Facebook improve its detection and enforcement mechanisms.

What possible excuse is there for something like this:

"This software trades for you on autopilot"

This statement alone raises several red flags and indicates a potentially misleading or even fraudulent advertisement. Here are some reasons why:

Unrealistic Promises: Trading on autopilot with guaranteed success is simply not possible. All forms of trading involve risk, and no software can completely eliminate the possibility of losses.
Lack of Transparency: The statement provides no information about the software's methodology, strategy, or track record. Legitimate trading tools would typically offer some level of transparency to build trust.
Potential for Scams: Such claims often lure unsuspecting individuals into investing in fraudulent schemes or subscribing to worthless software, resulting in financial losses.

It's crucial to remember that there's no shortcut to success in trading. It requires knowledge, skill, and a careful approach. Any advertisement promising effortless gains should be treated with extreme skepticism.

It's concerning that such misleading claims are allowed to circulate, and platforms like Facebook should take a more proactive approach to protect their users from potential scams.

There are deepfakes of Piers Morgan claiming that his journalism is just a hobby and that he makes all of his money from an online AI autopilot trading platform that saved his life when he needed money for medical treatment when he was bankrupt

There have been instances where deepfake videos of UK celebrities like Piers Morgan have been circulated, falsely claiming that they made their wealth from an AI autopilot trading platform rather than through their established careers. In these deepfakes, Morgan is depicted as saying that journalism is merely a hobby and that he turned to an online trading platform when he was bankrupt and needed money for medical treatment. The video then promotes a supposed AI trading platform, often part of a scam that attempts to lure victims into making investments or handing over sensitive financial information(Your Money)(AOL).

This type of scam is dangerous because it uses deepfake technology to create realistic footage of celebrities, making the claims seem more credible. Many victims fall for these scams, believing that a well-known figure like Morgan is genuinely endorsing the product. These deepfakes are commonly used in fraudulent ads and scam websites, especially on social media platforms(Your Money)(euronews).

If you come across such claims, it's important to verify their legitimacy by checking reliable news sources or the celebrity's official channels.

Here is an example of a scam:

Why Do So Many UK Celebrity Deepfake Adverts Appear on Social Media?