📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🎭 DEEPFAKE DETECTION FOR BEGINNERS FREE
Day 2 of 7 · 28% complete
Let me start with a story that changed the way I look at profile photos.
Earlier in 2026, a hiring manager was reviewing around 200 LinkedIn applications for an engineering role. Forty-three of the profile photos were AI-generated. The interesting part? She didn’t catch them herself. A junior HR team member noticed something unusual after about an hour of screening.
It wasn’t the eyes. It wasn’t the skin. It wasn’t even the hairline.
It was the ears.
In almost every suspicious image, something was wrong. One ear was missing because of the angle or hair. In other cases, the visible ear didn’t have the same anatomical detail you’d expect when you compared it with the other side. Once she started looking specifically at the ears, the pattern became surprisingly obvious. She flagged all 43 profiles, and 17 were later confirmed as bot accounts during additional checks.
What I find most interesting about that story isn’t the number 43. It’s how she found them. She didn’t need expensive forensic software or years of experience. She had learned one simple visual check and knew how to apply it systematically.
That’s exactly what I want to teach you today.
How to spot AI generated faces is a learnable skill. You don’t need some mysterious “AI instinct.” You need to know what to look for, understand why those details can go wrong, and build the habit of checking them in the same order every time.
That’s the approach I use when I’m examining suspicious images. Instead of staring at a face and asking, “Does this look fake?”, I break the image into specific zones and test each one.
Today, I’ll walk you through my complete 8-point checklist. We’ll look at the eyes and their catchlights, skin texture, hairlines, ears, teeth, lighting direction, background details, and the boundary between the face and neck.
I’ll also show you two free browser-based tools and, more importantly, explain what each tool can actually tell you — and what it can’t.
By the end of this lesson, I don’t want you to simply know that AI faces can contain artifacts. I want you to be able to open an unfamiliar profile photo, slow down for a few seconds, run through the checklist, and make a much more informed judgment about whether you’re looking at a real photograph or an AI-generated face.
🎯 What You’ll Master in Day 2
The 8-point visual artifact checklist for AI-generated faces
Why each artifact appears — the AI pipeline reason behind every tell
Eye reflection analysis — the single most reliable tell in the checklist
Two free detection tools in practice — Hive Moderation and FotoForensics
The 2026 update — which tells still work, which no longer do
⏱ 24 min read · 3 exercises · Free tool access needed 📋 Before You Start:
- Day 1 complete: What Are Deepfakes? — you need the GAN and diffusion basics before today’s artifact list makes sense
- A browser with 2 tabs — no installations required for any tool used today
- 15 minutes to try both free tools (Hive Moderation, FotoForensics) as you work through the checklist
How to Spot AI Generated Faces — Day 2 of 7
- Why Faces Are Readable — The Biological Asymmetry Advantage
- The Eye Test — Catchlights and the Specularity Problem
- Skin Texture — The Over-Smooth Tell
- Hairline and Boundary Blending
- Ears, Teeth, and the 3D Consistency Problem
- Lighting Direction and Background Coherence
- The 2026 Difficulty Update — What Still Works
- Questions and Answers
If you’ve ever searched for how to spot AI generated faces, you’ve probably noticed the same problem I did: most guides tell you to “look at the eyes” or “watch for strange skin” and then stop there. That’s not much help when you’re staring at a convincing 2026-generated portrait and trying to decide what, specifically, looks wrong.
So in this lesson, I’m going to do something different. I’m going to show you exactly what I look for, where I look for it, and why each area can reveal useful clues. We’ll work through the face zone by zone—eyes, skin, hairline, ears, teeth, lighting, background, and the face-to-neck boundary—so you have a repeatable process instead of relying on a vague feeling that something looks “off.”
I’ll also walk you through two free browser-based forensics tools that you can use alongside the visual checklist. The important part is knowing what the tools are actually telling you—and where their results can mislead you. Recent research makes that limitation especially important: human detection accuracy varies substantially by the type of synthetic image, while automated detectors can also struggle when they encounter generators or image conditions they weren’t designed for.
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