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Changing face using AI generated deepfake technology concept

A Generative Adversarial Network, or GAN, is a machine learning architecture that uses two competing neural networks: a generator and a discriminator. The generator creates fake data, while the discriminator tries to detect whether that data is real or fake. Over time, the generator gets better at creating realistic content, and the discriminator gets better at spotting it. This tug-of-war results in synthetic outputs that can appear indistinguishable from reality.Unlike traditional models that are trained to predict outcomes or classify data, GANs are designed to create. They learn by imitation, not instruction. This makes them especially powerful for generating synthetic media like images, video, and even audio.As GANs have evolved, so have their applications in synthetic content creation. Today’s most convincing deepfakes are often GAN-based. These are not just filters or effects. They are machine-generated representations that can simulate human faces, voices, and movements with near-perfect realism.Some examples include:StyleGAN: Generates photorealistic human faces that do not exist. CycleGAN: Translates one image style into another, often used in face-swapping. VoiceGAN and WaveGAN: Extend the concept to audio, enabling cloned speech and vocal mimicry. Cybercriminals are now using these tools to craft convincing impersonations, conduct fraud, and manipulate trust at scale. Voice deepfakes have already been used to trick employees into wiring funds. Video deepfakes have been deployed to mimic executives in live calls. The risk is real and already here.GAN-powered deepfakes introduce a new layer of threat to already complex cybersecurity environments. Here are some key risks:Synthetic Identity Fraud: Deepfake videos used to bypass biometric authentication systems. Executive Impersonation: Attackers using cloned videos and voices to approve fraudulent transactions. Disinformation and Social Engineering: GANs can mass-produce fake social media profiles and deceptive messaging. Bypassing Detection Systems: Traditional tools may fail to identify new, AI-generated threats due to a lack of known signatures. These risks are especially dangerous because they rely on trust. A voice heard over the phone, or a face seen on a screen, has historically been difficult to fake. GANs have changed that.Using GANs DefensivelyInterestingly, GANs can also be used to protect against deepfakes. Here are several ways defenders are turning the tables:1. Training Detection Systems Organizations use GAN-generated deepfakes to train detection algorithms. These synthetic samples help detection tools learn what to look for, improving accuracy and speed.2. Simulated Attacks and Red Teaming Security teams are using GANs to simulate deepfake attacks within their own environments. These tests help identify gaps in employee awareness and technical defenses.3. Watermarking and Integrity Checks Some developers are embedding invisible watermarks in genuine media files. These markers often break when passed through a GAN, helping identify tampering.4. Artifact Detection Even the best GANs leave tiny artifacts in their outputs. These imperfections might include unnatural lighting, eye movements, or subtle audio inconsistencies. Specialized tools can now detect these anomalies.&nbsp; Limitations and Ongoing ChallengesGANs are complex and difficult to interpret. This can make it hard to explain why a detection system flagged something as a fake. High-quality training data is essential. Many organizations do not have access to large, labeled datasets of deepfakes. There is an arms race between attackers and defenders. As detection systems improve, so do the methods used to avoid them. &nbsp;What should your organization do?Update Risk Models: Include deepfake-based attacks in business continuity and fraud planning. Combine Signals: Use behavioral analytics in conjunction with visual and audio tools to confirm identity. Educate Your Team: Train employees on how deepfakes work and how to respond when something feels off. Simulate and Stress-Test: Run internal scenarios using synthetic media to test your defense mechanisms. &nbsp; ConclusionGANs have unlocked new frontiers in both creation and deception. They are being used to attack and to defend, sometimes in the same enterprise. Organizations that want to stay secure in the age of synthetic media must treat deepfakes not as a future threat but as a present challenge. Understanding GANs is no longer optional. It is foundational to any serious cybersecurity strategy.

GANs (Generative Adversarial Networks): The Engine Behind Illusion

Darkweb, darknet and hacking concept. Hacker with cellphone. Man using dark web with smartphone. Mobile phone fraud, online scam and cyber security threat. Scammer using stolen cell.

Imagine getting a call from your CEO. You recognize the voice immediately. The number looks legitimate. The tone is urgent. There's a situation unfolding and you need to wire funds or reset a password. Everything feels normal. Until it isn't.That voice? It was never your CEO. That number? Completely spoofed. The request? Part of a coordinated cyberattack.This is not science fiction. It is already happening. Deepfake-enhanced vishing is one of the most dangerous evolutions in modern social engineering. And it is quickly becoming the preferred method for attackers who want to bypass your most hardened defenses by targeting the one thing no firewall can stop: trust.&nbsp; From Social Engineering to Synthetic DeceptionVishing, or voice phishing, has always relied on deception. A confident caller pretends to be someone they are not, hoping to manipulate the victim into handing over credentials, money, or access. For years, the weakness in these scams was the voice. Even skilled impersonators could not fully replicate someone’s tone or speech patterns well enough to fool anyone who actually knew the person.That limitation no longer exists.With just a short clip of recorded audio, attackers can now create synthetic voices that sound eerily realistic. These clones capture not only the pitch and accent, but also the pauses, inflection, and rhythm of natural speech. AI tools like ElevenLabs, Resemble, and a growing list of open-source models make this process quick and surprisingly easy.When combined with spoofed phone numbers that mimic official lines, attackers can completely recreate the illusion of a legitimate internal call. The victim sees a familiar number and hears a trusted voice. Their guard drops. The setup is nearly perfect.&nbsp; A Simple Formula with Serious ConsequencesIt starts with reconnaissance. Attackers identify potential targets, often focusing on executives or employees with access to money or credentials. They gather audio clips from public sources like LinkedIn videos, podcasts, or earnings calls. With just a few minutes of audio, they train a voice model that can speak anything they type.Then comes the setup. The attacker chooses a spoofed number, often matching the exact contact information of the person they are impersonating. They craft a believable story. Then they make the call.During the conversation, they create urgency. They reference known colleagues or company events. They push the target to act quickly. The objective may be to transfer funds, share a password, approve access, or install a malicious file. And because the voice sounds real, the victim often complies.&nbsp; The Real-World Damage Is Already HereA UK energy firm lost $243,000 after an executive received a call from what he believed was his parent company’s CEO. The voice requested a wire transfer to close a deal. The executive followed instructions. By the time he realized something was off, the money was gone.In another case, attackers impersonated a CFO during a video call and convinced employees to transfer $25 million. They used deepfake video and audio to simulate the executive’s presence. The attackers stayed off camera and claimed to have technical issues, which helped cover any inconsistencies.MGM Resorts suffered a major breach after attackers used voice phishing to impersonate employees and trick helpdesk staff. The result was a significant systems outage and tens of millions in losses. Meanwhile, penetration testing teams like Mandiant have successfully used cloned executive voices in red team exercises. Even trained IT staff have fallen for these simulations, installing fake updates and granting unauthorized access.&nbsp; How to Defend Against the UndetectableThere is no single solution. But awareness is the first line of defense.Employees need to understand that voices can be faked and phone numbers can be spoofed. Everyone in the company, from interns to senior leadership, should be trained to verify requests, especially those involving money, passwords, or system access.Use internal verification policies. Callbacks on known numbers. Code words for sensitive actions. Written confirmation through approved channels. A little friction can prevent a major incident.From a technical side, telecom providers are rolling out protocols like STIR and SHAKEN to make spoofing harder. AI-driven tools that analyze voice audio for signs of synthetic generation are also emerging. But adoption takes time, and these tools are not yet widespread.Until then, your culture is your control system. Make it normal to pause and verify. Make it acceptable to question authority if something feels off. And above all, make sure everyone knows that trust alone is not enough.&nbsp; Parallitical Can Help You Get Ahead of This ThreatWe work with organizations to simulate real-world vishing and deepfake attacks, so you can test your team, find gaps, and strengthen your response. Because in today’s world, social engineering is not just clever conversation. It is AI-powered deception. And the only way to stop it is to train like it is already here.Because it is.

Deepfake Vishing: Tools, Methodologies, and Defense

Abstract portrait of an humanoid and AI technology

Open Source Intelligence, or OSINT, has long been one of the most accessible and powerful methods for uncovering truth from publicly available information. Whether it involves monitoring news outlets, analyzing social media trends, digging into public records, or piecing together leaked documents, OSINT is all about making sense of the open internet. But over the past few years, the nature of that work has changed dramatically. Artificial intelligence is no longer just a buzzword. It has become the core engine driving a new era of open-source investigations.The volume of digital information we generate every day is staggering. Billions of posts, images, videos, emails, and other forms of content are uploaded or shared every 24 hours. For human analysts, keeping up with this kind of scale is impossible. This is where AI steps in. It allows OSINT professionals to sift through mountains of data, identify patterns, and surface insights at speeds that would have been unimaginable even five years ago.Large language models like GPT-4 are now used to read and summarize lengthy documents, extract key entities like names, organizations, and locations, and even generate hypotheses about what might be happening behind the scenes. These tools help break down language barriers, too. AI systems can translate content across dozens of languages in real time, allowing analysts to monitor social media or news sites from any region in the world.Computer vision is also making a major impact. AI tools can now recognize faces, objects, logos, vehicles, weapons, and even subtle visual clues from photos or video footage. A single frame from a video can be reverse-searched to find older or related versions, helping to expose reused or misattributed media. Facial recognition platforms like PimEyes have become powerful tools for tracking individuals across the internet using just a photo, while satellite imagery platforms, enhanced with AI, are being used to monitor troop movements, detect new construction, or verify damage from natural disasters or conflict.Perhaps the biggest change is in automation. OSINT workflows that used to involve dozens of manual steps can now be done almost instantly. Tools like IntelOwl allow teams to submit a single indicator—like an IP address or file hash—and receive a full report pulling from over a hundred data sources, including virus scanners, sandbox environments, and breach databases. Maltego, a popular investigation platform, now uses machine learning to automatically recognize entities and build relationship graphs between them, helping analysts map out networks of influence, ownership, or online behavior in just a few clicks.AI is also becoming essential in the fight against disinformation. Automated systems can detect bot networks, flag content with signs of coordinated manipulation, and identify when images or videos have been reused or altered. Deepfake detection algorithms look for inconsistencies in lighting, facial movements, or pixel patterns, helping to verify whether a piece of media is authentic or artificially generated.This isn’t science fiction—it’s already happening. During the Ukraine conflict, commercial satellite images were fed into AI models to count military assets and track movement. Investigators used AI to find social media posts tied to specific events and verify them against visual and geographic evidence. Journalists and analysts can now verify claims much faster, combining old-school OSINT tradecraft with cutting-edge AI tools.But this new frontier also brings new risks. AI can be fooled. It can misinterpret sarcasm, exaggerate threats, or hallucinate false connections. There’s also the risk of overreliance. When everything is automated, analysts might stop asking the hard questions. They might miss the human nuance that no algorithm can replicate. There are also serious concerns about privacy, legality, and ethics. Just because data is technically public does not mean it should be harvested, processed, and stored at scale without clear boundaries.At Parallitical, we believe the most effective OSINT in the age of AI will come from human and machine working together. Analysts bring context, ethics, and judgment. AI brings speed, scale, and pattern recognition. When used correctly, this combination is unmatched.AI is not replacing OSINT professionals. It is giving them superpowers. The future of open-source intelligence belongs to those who can harness AI’s full potential while staying grounded in sound investigative principles, legal awareness, and respect for the truth. As information warfare, cyber threats, and global crises grow more complex, the need for smart, responsible OSINT has never been greater.The tools are here. The data is out there. The real question is: what will you do with it?

Research

GANs (Generative Adversarial Networks): The Engine Behind...

Deepfake Vishing: Tools, Methodologies, and Defense

The Convergence: OSINT (Open Source Intelligence) and AI