Understanding AI and Deepfake Technology

There’s a pretty good chance that the shocking rate at which AI is advancing is out-pacing your cyber security training, policies and maybe even technologies. Have you addressed the use of AI and deep fakes in your cyber security policies? In a recent and alarming development that seems to have leapt straight from the pages of a science fiction novel, a Hong Kong based finance worker at a multinational firm was defrauded of $25 million, falling victim to an elaborate scam that employed deepfake technology to impersonate the company's CFO. This incident, which unfolded during a video conference call, marks a disturbing milestone in the intersection of cybercrime and AI, underscoring the urgent imperative for companies to bolster their cybersecurity frameworks, particularly against the backdrop of deepfake technology. The mechanics of the scam were deceptively simple yet devastatingly effective. The finance employee was lured into a video call with several participants, believed to be colleagues and the CFO, only to discover later that each participant was a digital fabrication. The deepfake avatars, mirroring the appearance and voice of real company personnel, instructed the employee to initiate a "secret transaction", leading to the unauthorised transfer of $25.6 million. This incident is not an isolated event but rather a harbinger of the potential threats posed by AI-driven disinformation and fraud. The use of deepfake technology to bypass facial recognition software, impersonate individuals for fraudulent purposes, and undermine the integrity of personal and corporate identities presents a clear and present danger. The case in Hong Kong, where fraudsters successfully manipulated digital identities to orchestrate financial theft, exemplifies the sophistication of contemporary cybercrime. The implications of this event extend far beyond the immediate financial loss. It serves as a stark reminder of the vulnerabilities inherent in digital communication platforms and the necessity for robust verification processes. The reliance on video conferencing and digital communication, accelerated by the global pandemic, has exposed systemic weaknesses ripe for exploitation. In response to this escalating threat, it is incumbent upon companies to adopt comprehensive cybersecurity strategies that address the unique challenges posed by deepfake technology. This includes implementing advanced authentication protocols, raising awareness and training employees on the potential risks of deepfakes, and deploying AI-driven security measures capable of detecting and neutralising synthetic media. As AI output become increasingly indistinguishable from reality, the line between authentic and artificial communication will blur, challenging individuals and organisations to navigate a new frontier of digital authenticity. It compels a reevaluation of the assumptions underpinning digital trust and identity verification, urging a proactive approach to cyber defence.

The New Corporate Threat: Deepfakes That Even Experts Can't Detect Welcome to the new reality where AI doesn’t just generate content, it manufactures convincing lies. You’ve probably seen it: - A CEO announces a fake acquisition. - A politician "says" something they never did. - A voice note "from your boss" requests a fund transfer. It all looks real. But it’s not. It’s a deepfake AI-generated audio, video, or images designed to deceive. Why it matters: Deepfakes are no longer just internet tricks or entertainment. They’re now: - Financial fraud enablers (voice clones used to scam employees) - Corporate risk vectors (fake news impacting stock prices) - Political weapons (manipulated clips used to sway public opinion) - Personal threats (identity misuse, blackmail, defamation) How to spot a deepfake  Look for: - Unnatural blinking or awkward lip sync - Plastic skin or weird lighting - Robotic tone or emotionless speech - Out-of-character statements - No credible source backing the video If it feels off, it probably is. What you can do: - Pause before sharing - Use tools like Deep ware, Microsoft Video Authenticator, or Adobe Verify - Train your teams especially PR, legal, and finance - Push for content provenance in your organization In the GenAI era, trust is currency. Don’t spend it on content you didn’t verify. #artificialintelligence

Last year, MyHeritage’s Deep Nostalgia stunned the world by bringing old photos to life. Using AI, it animated faces, making long-lost relatives blink and smile as if frozen moments in time had been reawakened. The technology was both awe-inspiring and unsettling, raising questions about how artificial intelligence can blur the lines between memory and reality. But what powers this kind of deep learning magic? At the heart of it all is Generative Adversarial Networks (GANs), a groundbreaking AI model introduced by Ian Goodfellow in 2014. GANs operate through a competitive process between two neural networks, one that generates images and another that tries to distinguish real from fake. This back-and-forth learning method has pushed AI to generate stunningly realistic images, deepfakes, and even original art. What started as an experiment in image synthesis has evolved into a powerful tool reshaping industries from entertainment to medicine. But how exactly do GANs work, and why do they matter? Beyond their ability to generate high-quality visuals, they play a crucial role in AI advancements, from data augmentation to video enhancement. However, they also come with challenges, including high computational costs and ethical concerns over synthetic content. Let’s take a deeper look at the mechanics of GANs, their real-world applications, and the implications of this evolving technology.

The International AI Safety Report 2026 launches today! It was a privilege to contribute to this work, alongside an Expert Advisory Panel representing 30+ countries, and fellow reviewers across industry, academia, and civil society. This report synthesizes the evidence on the capabilities and risks of advanced AI systems. It aims to support informed policymaking globally by providing an evidence base for decision-makers. My contributions focused on harm to individuals through fake content, particularly through malicious uses including AI-generated content and criminal activity, criminal uses of AI content, mitigations, and challenges for policymakers. Key highlights of the report include: ▶️ General-purpose AI capabilities have continued to improve rapidly, especially in mathematics, coding, and autonomous operation. In 2025, leading AI systems achieved gold-medal performance on International Mathematical Olympiad questions, exceeded PhD-level expert performance on science benchmarks, and became able to autonomously complete some software engineering tasks that would take a human programmer multiple hours. Performance nevertheless remains "jagged," with systems still failing at some seemingly simple tasks. ▶️ AI adoption has been swift, though uneven globally. AI has been adopted faster than previous technologies like the personal computer, with at least 700 million people now using leading AI systems weekly. In some countries, over half of the population uses AI, though across much of Africa, Asia, and Latin America, estimated adoption rates remain below 10%. ▶️ Incidents related to deepfakes are on the rise. AI deepfakes are increasingly used for fraud and scams. AI-generated non-consensual intimate imagery, which disproportionately affects women and girls, is also increasingly common. For example, one study found that 19 out of 20 popular "nudify" apps specialise in the simulated undressing of women. ▶️ Biological misuse concerns have prompted stronger safeguards for some leading models. In 2025, multiple AI companies released new models with heightened safeguards after pre-deployment testing could not rule out the possibility that systems could meaningfully help novices develop biological weapons. ▶️ Malicious actors such as criminals actively use general-purpose AI in cyberattacks. AI systems can generate harmful code and discover vulnerabilities in software that criminals can exploit. In 2025, an AI agent placed in the top 5% of teams in a major cybersecurity competition. Underground marketplaces now sell pre-packaged AI tools that lower the skill threshold for attacks. ▶️ Many safeguards are improving, but current risk management techniques remain fallible. While certain types of failures, like "hallucinations," have become less common, some models are now capable of distinguishing between evaluation and deployment contexts and can alter their behaviour accordingly, creating new challenges around evaluation and safety testing.

In 30 seconds, AI showed me patrolling Midtown, flexing with a private jet, and watching sunset at the Pyramids—yet I never left my chair. As a company that builds this technology, we watch clips like these every single day. Some are brilliant; some are unsettling. Over time, we’ve learned to spot the giveaways—tiny lighting glitches, off‑beat lip movements, location jumps you only notice on the tenth viewing. To share that hard‑earned pattern‑recognition, we’ve compiled the State of Deepfakes Report: What’s inside? A field guide to the four generations of deep‑fake video, from simple face swaps to the near‑flawless, long‑form scenes now emerging. The tell‑tale signs for each generation—what breaks first when reality is synthetic. Real‑world misuse cases we’ve already encountered, and the safeguards that work (or fail). Why publish it? Because the people best positioned to expose the risks are the ones building the tools. Because watermarking and provenance standards aren’t universal—yet. Until they are, collective awareness is our strongest defense. If AI can make me do all that on screen, it can fabricate a confession, a crisis update, or a world‑shaking headline just as easily. Knowing what’s technically possible—and what subtle errors still slip through—helps all of us judge what we see with clearer eyes. 🔗 The full report is linked in the first comment. Read it, share it, and let’s keep the conversation grounded in facts, not hype. #Deepfakes #ResponsibleAI #MediaIntegrity #Transparency #PublicSafety

AI now sits between you and almost everything you do online. It decides what you see, recommends what you buy, summarizes what you read, and soon will act on your behalf. You don’t need to understand how it works for it to shape your behavior - it already does. While most people focus on how to use AI, few recognize how easily it can also be used against them. AI can generate videos, images, and voices so realistic they can impersonate anyone: friends, colleagues, even world leaders. Deepfakes and synthetic personas are deployed to deceive, scam, and manipulate with precision. Trust is becoming an early casualty of automation. Scams have evolved from crude phishing emails to emotionally engineered conversations. AI can clone a voice, mimic tone, and adapt in real time, making every message feel authentic. One careless click can expose data, money, or access - not to a lone hacker, but to automated systems running thousands of attacks in parallel. Hackers no longer need months to find vulnerabilities. They use AI to scan networks, generate exploits, and breach systems faster than humans can react. Models themselves can be compromised through prompt injection, poisoned data, or memory leaks. One exploited model can ripple across infrastructures before anyone notices. AI thrives on personal data: the texts, images, and creative work we’ve shared for years. Much of that digital footprint is now repurposed to train models that can recreate your likeness, your voice, or your writing style without consent. At the same time, AI supercharges surveillance. Cameras, sensors, and algorithms can track movement, behavior, and communication at unprecedented speed and scale. What once took teams of analysts now happens automatically and invisibly. Even creativity is caught in a loop. AI learns from human output, replicates it, and feeds it back into new systems. As synthetic content trains future models, the internet risks becoming a self-replicating archive of imitation - a feedback cycle where originality and authenticity erode. AI already drives decisions in healthcare, finance, logistics, and critical infrastructure. A single failure can misdiagnose a patient, disrupt markets, or halt production. These aren’t hypotheticals - they’re systems being automated faster than they’re being secured. The same tools that power progress can also weaken control. AI reflects both our intelligence and our vulnerability, amplifying everything we build into something larger than we can fully oversee. Individuals must protect their data, identity, and voice. Organizations must secure their models, audit data flows, and implement oversight. And those building AI must design systems that remain transparent, verifiable, and aligned with human purpose. This is the next phase of digital responsibility: ensuring the intelligence we create continues to work for us, not against us.

58% of people can't tell deepfakes from real content. I've been tracking this technology for months, and the sophistication is alarming. Here are the 5 types you need to recognize: 1. Face Swap Deepfakes Replace one person's face with another's in videos. Politicians and celebrities are common targets. 2. Speech Synthesis Clone someone's voice using just minutes of audio samples. Your favorite podcaster might not be speaking those words. 3. Full Body Puppetry Control entire body movements and gestures. Think digital doubles performing actions the real person never did. 4. Text-Based Deepfakes Generate fake written content that mimics someone's writing style perfectly. → LinkedIn posts, emails, articles. 5. Real-Time Deepfakes Live video manipulation during calls or streams. The person you're video chatting with might not be real. The scary part? These tools are becoming accessible to everyone. What used to require Hollywood budgets now runs on consumer laptops. My defense strategy: → Verify through multiple sources before sharing → Trust your instincts when something feels off → Look for unnatural blinking patterns in videos → Check for inconsistent lighting on faces → Listen for robotic speech rhythms The technology isn't slowing down. Your awareness needs to speed up. P.S. What's the most convincing fake content you've encountered recently? Here’s my AI Awareness Guide that shows you how to protect yourself from deepfakes and AI scams. https://lnkd.in/eZeGbmia

In a world where our eyes and ears once felt like the ultimate fact-checkers, we’re now grappling with a reality where seeing (or hearing) might not be believing. AI-generated images and videos - often called “deepfakes” - have surged in realism, making it harder to distinguish factual evidence from fabricated clips. Not long ago, an AI-driven “Barack Obama” speech circulated to highlight the potential for disinformation. That was just a glimpse of things to come. Between 2019 and 2020, researchers noted a massive jump in deepfake content, and some estimates suggest that by the end of 2026, much of what we see online could be generated or altered by AI. It’s not all doom and gloom. These same AI tools can lead to immense creativity. Artists and educators can craft innovative, immersive experiences; filmmakers can resurrect historical figures on screen; and game designers can construct lifelike virtual worlds. The challenge is balancing these positive applications with the risk of malicious misuse. Recent misuses of the generative AI models - That stylish photo of the pope in a striking white coat? AI-made - and it tricked thousands online before the truth emerged. - During a conflict, a fabricated video of the president of Ukraine calling for surrender made the rounds. While poor quality gave it away, better-crafted versions in the future may not be so obvious. - Criminals have successfully replicated executives’ voices, tricking bank managers into transferring large sums of money - proving audio deepfakes can be more than a mere nuisance (e.g. a case in Hong Kong). - And now the DeepFake community is taking aim at the recent Oval Office incident by creating a fake fight video. Although this particular video is clearly fake, I have encountered other DeepFake content of the same event that's been expertly edited after generation, making them quite convincing to general public. These aren’t hypothetical problems. They’re already influencing society, security, and our collective sense of what’s real. Possible Ways Forward - Media Literacy: Equip people with the skills to identify suspicious content. A discerning public is the first defense against fake media. - Transparency Tools: Encourage labeling for AI-generated content - like digital watermarks or metadata - to help users spot synthetic media. - Thoughtful Regulation: Governments and tech companies can set guardrails without stifling legitimate artistic or creative endeavors. The goal is to curb misuse while allowing innovation to flourish. We may not be able to rewind technology’s progress, but we can learn to navigate it responsibly. By staying informed and involved, we can shape a future where AI’s creative potential thrives - and where misinformation loses its foothold. How can we encourage the exciting possibilities of AI-driven art and media while cutting down on the spread of deceptive content? #innovation #technology #future #management #startups

From presidential robocalls to explicit images of Taylor Swift, deepfakes - hyperrealistic AI-generated video, photo, and audio forgeries - have surged online reaching a staggering 95,820 documented deepfake videos in 2023 alone. This blurring of reality requires teaching a new approach to vetting if online content is real or fake. So we've created our newest downloadable guide designed to build student awareness of the presence and impact of deepfakes, while providing key discussion topics on the ethics of AI-generated content. The Guide features: - Information on what Deepfakes are and their impact on society - Strategies for critical consumption of online content and the identification of deepfakes - Key examples of audio, photo, and video deepfakes for the classroom - Guiding questions for classroom discussion on the ethics of deepfakes You can view the guide or download a PDF version here: https://lnkd.in/eNep3Jjy. AI for Education #aiforeducation #aieducation #responsibleAI #GenAI #Deepfakes

I had the opportunity to discuss the complex landscape of deepfakes, particularly those involving synthetic voices, on the RTL Hrvatska news channel with Tea Mihanović. This rapidly evolving technology, powered by artificial intelligence, has the ability to create realistic audio and video content that can mimic real individuals, presenting both exciting opportunities and significant threats across various sectors. In the entertainment and media industries, synthetic voices can open up new creative possibilities, such as enhancing character experiences in gaming or improving customer service interactions. However, these advancements also raise concerns about the ethical use of this technology. On the flip side, the rise of synthetic voices has facilitated new forms of fraud, such as “vishing,” where criminals use cloned voices to deceive individuals into revealing sensitive information. This poses serious risks to financial institutions and personal security, as voice-based biometric systems can be easily compromised. The ability to replicate voices has also enabled new avenues for identity theft and fraud, as demonstrated by my own experiment where I was able to generate a fake call for help in the identical voice of a reporter. This highlights the urgent need for protective measures against non-consensual deepfake creation. As we navigate this complex landscape, it’s crucial to embrace the potential of AI while also addressing the ethical and security challenges it presents. I encourage everyone to stay informed and engage in discussions about the responsible use of these technologies. If you’re interested in exploring specific use cases or sharing your insights, I welcome the opportunity to connect and explore this topic further. Are you ready to dive deeper into the world of deepfakes and synthetic voices? #AI #Cybersecurity #Deepfakes