Backend Engineering Manager at Fiverr & Founder of FauxLens
Netanel Ossi is a Backend Engineering Manager at Fiverr and the founder of Faux Lens. With deep expertise in distributed systems, security protocols, and backend architecture, he leads the development of advanced AI image detection and forensic analysis tools.
A detailed guide to the digital forensics of AI image detection, from Error Level Analysis (ELA) and JPEG compression artifacts to Photo Response Non-Uniformity (PRNU), and which checks a FauxLens scan actually runs.
Before you run a scan, check these 5 common mistakes AI models make. From topology errors in hands to subsurface scattering fails in skin.
Scammers are using AI to create entire fake personas. From bypassing KYC checks to the 'Romance Scam' economy, learn how synthetic identities are reshaping fraud.
Most tools in this space keep your uploads. FauxLens does not: your file is deleted as soon as the analysis ends. Here is why, and what is and is not kept.
A technical showdown between the major AI engines. This guide breaks down the specific 'digital fingerprints' associated with Midjourney v6, DALL-E 3, and Stable Diffusion.
The word 'deepfake' entered mainstream vocabulary. But most people still don't fully understand what it means or how dangerous it's become. This guide covers everything: the technology, the harms, and the detection.
AI-generated images are flooding newsrooms. Here is the step-by-step verification workflow used by digital forensics professionals and investigative journalists to separate real from synthetic.
AI video generation has crossed a threshold. Sora, Veo 3 and Kling now produce clips the untrained eye cannot separate from real footage. Here is what forensic analysis can still find, why a re-encoded clip can look more authentic than the original, and where video detection genuinely fails.
Every AI model leaves an invisible fingerprint in the images it generates. GAN fingerprints are the forensic equivalent of ballistic markings-unique to the weapon, invisible to the eye, detectable by science.
When a detector says '97% AI-generated,' what does that actually mean? Understanding confidence scores, base rates, and the difference between a useful signal and a false certainty.
Every photo your camera takes contains a hidden layer of data: camera model, GPS coordinates, time of capture, and more. AI-generated images usually lack this layer. Here's how to read it.
AI has made romance scammers more convincing than ever. FBI data shows over $1.3 billion lost annually. Here is the visual checklist every person on a dating platform needs before trusting a profile.
Tinder, Bumble, and Hinge are in an arms race against synthetic identity. From selfie verification to real-time liveness detection, here is what the platforms are doing, and what they're still missing.
Adobe, Microsoft, OpenAI, Sony, and the BBC have united behind a single open standard for proving media authenticity. C2PA embeds cryptographic provenance into images and videos. Here is what it means for the future of truth.
You found a suspicious image. Now what? This step-by-step guide walks you through every method to determine whether a photo is real or AI-generated, from visual inspection to forensic analysis.
Stable Diffusion is open-source, infinitely customizable, and runs locally. That makes it the hardest AI model to detect. Here is how forensic analysis identifies SDXL and its fine-tuned variants.
Flux, from Black Forest Labs, makes AI pictures that can pass for real photos. Here are the faint clues it still leaves, in plain words, and how detectors look for them.
Building a platform that needs to verify image authenticity? This technical guide covers API integration for AI image detection, from authentication to response handling to production deployment.
Not sure if a photo is real? This guide covers five proven methods to check if a picture is fake, from visual tells to forensic tools, with step-by-step instructions for each.
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