How to Use AI to Detect if an Image Was Created by Midjourney or DALL-E 3: Practical Guide 2026

In 2026, the ability to detect AI-generated images has become an essential skill for anyone navigating the internet. Every day, millions of images generated by systems like Midjourney, DALL-E 3, and Stable Diffusion circulate on social media, blogs, and news outlets. Although these artificial intelligence tools have advanced tremendously in realism, reliable methods still exist to identify whether a photograph is authentic or synthetic. This practical guide will teach you concrete techniques, verified tools, and step-by-step strategies to detect AI-generated images with confidence. Whether you’re a journalist, community manager, digital researcher, or simply someone concerned about visual misinformation, mastering these skills will protect you from manipulation and fraud.

Detection Method	Effectiveness	Difficulty	Tools
Manual visual analysis	High (v1-v2 images)	Low	None
AI detection tools	Medium-High (80-90%)	Very Low	Midjourney Detector, Sensity
EXIF metadata analysis	High (real photos)	Medium	ExifTool, InVID
Reverse image search	Medium (source tracking)	Low	Google Images, TinEye
Physical inconsistency analysis	High	Medium-High	Trained human eye

Visual signs that reveal an AI-generated image

Learning how to detect AI-generated images begins with training your eye to recognize characteristic visual artifacts. Although 2026 models are significantly better than their predecessors, they still present inconsistencies that give them away. The most common factor is distortion in small and complex elements, especially in areas requiring anatomical precision.

Hands remain the most obvious weakness in images generated by Midjourney and DALL-E 3. A human face might appear perfect, but if you look carefully at the fingers, you’ll find incorrect numbers, impossible joints, or fused digits. This error persists because AI struggles to process multiple limbs with correct proportions.

Other common visual artifacts include:

• Illegible or invented character text on signs and labels
• Light reflections that don’t match the light source
• Blurry transitions between near and distant objects
• Misaligned or unnaturally shaped teeth
• Eyes with disproportionate or asymmetrical pupils
• Excessive facial symmetry (too perfect)
• Artificial or uniform skin texture
• Ears with incorrect proportions relative to the head

In 2026, models like DALL-E 3 v4 and Midjourney v6+ have improved significantly, but still produce what experts call “visual uncanny valley”: images look almost perfect, but something indefinable seems wrong. This effect is difficult to describe, but after analyzing dozens of images, your brain will develop intuition to detect it.

An effective technique is to examine lighting and shadows. Shadows in real images always respect physics: they have defined edges (hard shadow) or soft (penumbra), and their intensity decreases gradually. Shadows generated by AI frequently show abrupt transitions or present shadows of objects that don’t exist, especially in complex scenes with multiple elements.

💡 Tip: Open the image in an editing tool like Canva Pro (which includes visual quality analysis) and zoom to 400% to inspect small details. Errors usually concentrate in low-focus areas.

Automated tools to detect AI-generated images

Although manual analysis is valuable, detecting AI-generated images with greater precision requires specialized tools. In 2026, detectors have emerged specifically trained to recognize unique characteristics of Midjourney, DALL-E, Stable Diffusion, and other models. These tools use neural networks that analyze pixel patterns imperceptible to the human eye.

Official Midjourney Detector (2026): Midjourney launched its own detector in 2025, optimized to identify images from its platform with 94% accuracy. It works by analyzing the unique digital signature the model imprints on each generation. You can access it through the Midjourney dashboard or via its API.

Sensity AI (Now Part of Reality Defender): This is the most reliable tool for detecting deepfakes and generated images. It offers detailed analysis indicating confidence levels and highlighting which image areas appear manipulated. The professional version allows processing 1,000+ image batches.

Google DeepFake Detector: Google has included detection technology in Google Lens since 2024. When you upload an image, the system checks against its database and notifies you if severe manipulation or synthetic features are detected.

InVID Browser Extension: Although primarily for video verification, the InVID extension for Chrome and Firefox includes very useful image metadata analysis. It lets you see when it was uploaded, where, and if earlier versions exist online.

Other effective 2026 tools:

• AI Image Detector by Hugging Face – Free, trained on open-source models, good for Stable Diffusion
• Optic (by Truepic) – Image authenticity certification, useful for professionals
• Forensically (Zoom.ai) – Image forensic analysis with cloning and manipulation detection
• JPEGsnoop – Technical JPEG compression analysis to detect edits

The main limitation of these tools is that they are not 100% accurate with very new models. As DALL-E and Midjourney improve, some detectors fall behind. That’s why combining manual analysis + automated tools is the most effective strategy.

⚠️ Warning: No tool is perfect. Images generated with the latest DALL-E 3 and Midjourney v6+ versions may evade some detectors. Always combine multiple methods.

EXIF metadata analysis: the forensic method

One of the most reliable methods to determine if an image is from AI is examining its metadata. Real photographs taken with digital cameras (smartphones, DSLRs, mirrorless) contain EXIF data recording the time, GPS location, camera model, ISO setting, shutter speed, and many technical details. AI-generated images rarely contain authentic data like this.

Step 1: Download ExifTool

ExifTool is a free, open-source tool available for Windows, Mac, and Linux. Download it from exiftool.org. It’s the standard tool used by media researchers worldwide.

Step 2: Extract the metadata

Place the image in the same folder as ExifTool. Open the terminal/cmd and run: exiftool image.jpg. A complete report with all metadata will display.

Step 3: Analyze key data

Look for these specific fields:

• Make/Model: Does a real camera appear (Canon, Sony, iPhone) or is it empty?
• DateTime Original: Does it match the expected date?
• GPS Info: Does it have precise GPS coordinates?
• Focal Length/Aperture/ISO: Do they have realistic values?
• Software: Does it mention Midjourney, DALL-E, or AI generators?

If an image supposedly taken with an iPhone 15 Pro lacks Apple EXIF data, it’s suspicious. If the “Software” field mentions “Midjourney” or “DALL-E API”, it’s definitely generated.

Step 4: Verify post-processing changes

Real images edited in Photoshop or Lightroom show evidence of editing software. AI-generated images, even after light editing, frequently lack certain metadata that Photoshop always adds. This is especially evident with Canva Pro, which leaves unique traces in the metadata of anything it processes.

Results interpretation:

A genuine image typically shows:

• Complete EXIF data with real camera information
• Coherent creation dates
• Realistic technical values (ISO 100-6400, f-stop 1.4-f/22)
• Often contains GPS data or known editing software data

An AI-generated image typically shows:

• Minimal or completely absent EXIF data
• Software listed as AI generator or empty
• Creation dates frequently recent (today or this week)
• No GPS data

💡 Pro Tip: Some AI generators now inject false metadata to evade detection. That’s why EXIF analysis must combine with other methods. If an iPhone XS supposedly generated the photo according to metadata, but it contains typical DALL-E rendering errors, it’s suspicious.

Reverse image search: tracking the origin

Detecting AI-generated images also means understanding their origin. A real image will likely circulate on multiple platforms, be published in articles, and have a traceable history. Generated images usually appear suddenly on social media without prior history. Reverse search is your ally.

Method 1: Google Images (Free)

Visit images.google.com, click the camera icon, upload the image or provide the URL. Google will show you where that image appears online and when it was first indexed. If it appears originally in 2026 without earlier versions, it’s probably generated or recent.

Search for results showing the image in credible contexts: news articles, verified social media profiles, established news sites. If the image appears only in dubious sources or in thousands of simultaneous copies, it’s suspicious.

Method 2: TinEye (Specialized)

TinEye (tineye.com) is more specialized than Google for reverse search. It has a different database and often finds results Google misses, especially older image versions. TinEye also detects image transformations (rotations, crops, color adjustments).

TinEye will show you exactly what changed in the image and when. If an image has been modified 50 times across different platforms without a clear original version, it could be generated.

Method 3: Bing Visual Search

Bing has its own visual search engine with results different from Google. Visit Bing.com/images, click the camera. Sometimes it finds posts or sources that Google doesn’t index.

Interpreting reverse search results:

Signs of authentic image:

• Appears in Google Images with varying publication dates (some results from 2023, others from 2024, etc.)
• Sources include recognizable media: Reuters, AFP, Getty Images, news publications
• The “original version” can be traced to an identifiable photographer or agency
• Multiple variants: different crops, varied sizes, edited in different ways

Signs of generated or fake image:

• No results in Google Images or only identical exact copies
• Appears only on social media, without credible news sources
• All copies are identical (no crop or edit variants)
• Simultaneous posts across unconnected accounts
• Only appears on new blogs or accounts (less than 6 months old)

ℹ️ Information: If you want to verify content on Wikipedia or articles generated by AI, we have a specialized guide: How to Use AI to Detect if a Wikipedia Article Was Written by ChatGPT. The same verification principles apply to visual content.

Advanced technique: analyzing physical and anatomical inconsistencies

To fully master how to detect AI-generated images, you need to train your eye to detect violations of basic physics and human anatomy. This analysis requires more knowledge, but it’s extremely powerful when mastered.

Light consistency analysis:

Examine where light originates in the image. In an authentic photograph, everything has coherent light source. If a person is lit frontally but their shadow points forward, the physics is impossible. In AI-generated images, objects frequently have shadows suggesting multiple contradictory light sources, or shadows that simply don’t exist.

Look for these specific lighting errors:

• Pupils not reflecting the main light source
• Shadows on objects that don’t cast shadows (like air)
• Shiny objects without realistic specular highlights
• Lighting on one side of the face without realistic shadow transition on the other side

Human proportions analysis:

Human anatomy follows mathematical rules. The head should be approximately 1/7 of total height. Extended hands should almost reach the knees. Eyes are approximately halfway between the crown and chin. Ears should align with eyes and jawline.

AI-generated images frequently violate these proportions subtly. Use the “line check” technique: open the image in an editor (Canva Pro is excellent for this), and draw lines to verify alignment. If the left eye is slightly higher than the right without justification (like head tilt), it’s suspicious.

Hand and finger detail analysis:

Hands remain AI’s Achilles heel in 2026. Inspect:

• How many fingers are there? (Miscounting is still common)
• Do finger joints align correctly?
• Do nails have realistic shape and size?
• Is relative finger length correct? (Ring finger usually longest)
• Do hands show veins and realistic texture?

Texture and micro-detail analysis:

Real images show texture complexity. Human skin isn’t smooth: it has pores, small imperfections, color variation. Hair contains hundreds of individual strands. Clothing has folds and wrinkles specific to fabric and movement.

Generated images frequently “smooth” these textures excessively or, conversely, create uniformly noisy texture. DALL-E tends to create more uniform textures. Midjourney is better, but still shows inconsistencies upon close inspection.

Background and context analysis:

Generated backgrounds are frequently where AI commits most obvious errors. Look for:

• Partially formed objects or blurry edges without reason
• Impossible architecture (nonsensical windows, oddly angled doors)
• Illegible or invented characters on signs or labels
• Incorrect perspective (distant objects appearing larger than near ones)

💡 Training Exercise: Spend 30 minutes analyzing Midjourney and DALL-E images on Reddit (r/midjourney, r/dalle) where people post their creations. Then analyze real photos from Getty Images or news publications. Your brain will rapidly develop the ability to detect subtle differences.

Protection against deepfakes and visual manipulation on social media

Detecting visual manipulation on social media goes beyond simply identifying generated images: you need to understand how bad actors use this technology for misinformation. In 2026, video deepfakes and image manipulation have become common tools for fraud, extortion, and political misinformation.

Current types of visual manipulation:

1. Completely generated images: Faces, entire scenes created from scratch with Midjourney or DALL-E 3. Purpose: create false testimonies, evidence of “nonexistent” events.

2. Face-swapping: Tools like DeepFaceLab replace one person’s face with another’s. Purpose: create videos of people saying things they never said.

3. Selective manipulation: Editing real images to change context or meaning. Purpose: change an image’s message without being completely false.

4. False composition: Combining real images from different events into a single image. Purpose: suggest unrelated events are connected.

Protection strategy on social media:

Step 1: Verify original source

Before sharing any viral or impactful image, find the source. Is it published by a verified news site? A credible professional photographer? Or just influencers with no visible connection to the event?

Step 2: Look for multiple angles

Real important events are captured from multiple angles by multiple sources. If only one image version exists of an important viral image, it’s suspicious. Real reporters have multiple image sources.

Step 3: Verify temporal context

Generated images frequently appear without historical context. Use reverse search with date filters. If an image of a “natural disaster” first appears on social media Tuesday without news coverage from Monday (when it should have happened), it’s suspicious.

Step 4: Look for AI-specific “red flags”

When you see images on social media involving:

• Celebrities or public figures in embarrassing situations
• Controversial political events
• Impactful disasters or crimes
• Emotional testimonies from “real people”

Apply extra scrutiny. These are the categories where manipulation is most common and harmful.

Verification tools for social media:

The InVID extension for Chrome/Firefox integrates reverse search directly. You can right-click any social media image and verify immediately. If you’re a community manager or work with visual content regularly, consider tools like Grammarly (which, though primarily for text, integrates with visual content analysis in shared documents) and professional verification platforms like Truepic.

ℹ️ Related Reading: If you’re concerned about information manipulation generally, our article How AI Manipulates Your Digital Memory: Guide to Detecting Poisoned Information in ChatGPT and Claude covers broader misinformation techniques.

Key differences between real and AI-generated images

To develop intuition about what differences exist between real and AI-generated images, you need to understand fundamental limitations of how generative models work. In 2026, technology has advanced tremendously, but image generators still operate with principles that give them away.

Real images capture actual light: A photograph is a record of photons bouncing off real objects. This light contains infinite information: multiple wavelengths, specular reflections, diffraction. When we talk about “depth of field”, optics create it naturally.

AI images predict likely pixels: Models like DALL-E and Midjourney operate through transformers: they predict which pixels should be here based on statistical patterns. They’re incredibly good, but it’s probabilistic prediction, not real light capture.

Practical consequences of these differences:

Feature	Real Image	AI-Generated Image
Sensor noise	Realistic random pattern in dark areas	Smooth or artificial noise, or completely absent
Chromatic aberration	Slight RGB shift at edges (real lenses)	Generally absent or inconsistent
Light diffraction	Realistic halos around bright sources	Often too perfect or absent
Movement and flow	Natural movement with consistent motion blur	Movement usually seems frozen or artificial
Materials and reflections	Complex specular reflections, realistic glass	Reflections frequently simplified or incorrect
Depth of field	Natural bokeh with brightness variation	Smooth bokeh but often uniformly smooth

Internal consistency: Real images have coherent “visual story”. If you see a person in sunlight, their shadow has consistency. If it rains, pavement is wet. Generated images sometimes violate these subtly.

Controlled complexity: Real images of chaotic events show genuine complexity. Generated images often “simplify” background details even when the prompt requested complexity. It’s as if AI says “this is too complicated, I’ll make a smooth background”.

Invariant features: In real images, object identity doesn’t change by angle. A face is recognizable from different angles. In generated images, particularly if the same image is edited or rotated, feature consistency (like facial patterns) can degrade.

💡 2026 Insight: The battle between detection and generation is constant. As Midjourney v6+ and DALL-E 3 improve, these difference points become more subtle. That’s why tools like EXIF analysis (which verify fundamental metadata reality) will become increasingly important than pure visual analysis.

Troubleshooting and difficult cases

After applying all previous methods, you’ll still encounter ambiguous images where it’s hard to determine if they were created by AI or are real. This section covers the most challenging 2026 cases.

Case 1: Midjourney v6+ or DALL-E 3 images with “quality high”

Latest model versions are so good that even experts struggle. If an image was generated with these parameters, your best strategy is:

• Verify EXIF metadata (if completely absent, probably generated)
• Use multiple AI detectors (Sensity, Google, Midjourney Official)
• If >3 independent detectors say “is AI” with confidence >80%, it’s probably correct
• Reverse search: if it doesn’t exist online before the suspicious date, probably generated

Case 2: Real images partially edited with AI tools

This is most difficult: a real photograph enhanced, inpainted, or edited with AI tools. Someone might take a real landscape photo, then use DALL-E “inpaint” to improve the sky, or use Photoshop with Adobe Firefly to fill missing details.

Indicators:

• Some elements seem realistic, others slightly “off”
• There’s a clear line or transition where editing occurred
• Sensor noise is consistent except in one region

Strategy: These images are technically “partially false”. For news verification purposes, consider that if the critical image region was edited by AI, it’s problematic. Tools like Forensically (zoom.ai) can detect splicing and edits.

Case 3: Generated images of real historical events

Someone uses Midjourney with prompt “Normandy Landing, D-Day, 1944, historical photograph”. The image looks realistic because it’s based on real era photography style. But it’s completely generated.

Reverse search doesn’t work. But:

• AI detectors work reasonably well
• Metadata: if the “1944” image has 2026 metadata, it’s fake
• Historical inconsistencies: incorrect uniforms, anachronistic equipment

Case 4: Generated images that deliberately include AI “red flags”

Some artists intentionally generate images that seem AI-generated as commentary. For example, horribly deformed hands as commentary on AI limitations. In these cases, the question “is it AI?” is academic: admittedly generated, but it’s art, not fraud.

In verification context, the important question is “does it claim to be real when it’s not?”. If the artist labels their image as “Generated with Midjourney”, it’s not misinformation.

Step-by-step troubleshooting:

Problem: The detector says “is AI” with 95% confidence, but reverse search finds the image in a 2023 Reuters news article.

Solution: Reuters occasionally publishes images later discovered to be manipulated. Verify the original article. Is the image still there? Did Reuters publish a retraction? Search Google News “Reuters retracted image 2023”. Retractions are publicly documented.

Problem: Image completely lacks EXIF metadata, but has all visual artifacts of a real photograph.

Solution: It’s possible. Images downloaded from social media frequently lose metadata. Download from the most original source possible. If it’s from Twitter, download from Twitter. If it’s from an article, download from the article’s server. The file server sometimes retains metadata that social media strips.

ℹ️ Note on Additional Tools: If you regularly work with content analysis, platforms like Canva Pro offer integrated visual analysis features that can complement your workflow. Also, consider exploring our article on How to Use AI to Detect if an Article Was Written by Claude or Gemini, which covers complementary verification methods for text content that frequently accompanies false images.

Best practices and final recommendations for 2026

We’ve covered extensively how to detect AI-generated images from multiple angles. Now let’s consolidate everything into a practical system you can implement immediately.

Quick verification workflow (5 minutes):

1. Visualization: Look at the image for 30 seconds. Does something feel “off”? Odd hands, impossible lighting, blurry background?
2. Reverse search: Google Images (1 minute). Where does it appear? Credible sources?
3. Context: Who’s sharing this? Is it a known source? (1 minute)
4. Decision: Based on the previous 3 steps, is it probably real or probably generated? (2 minutes)

This process takes ~5 minutes and is sufficient for most cases. For deeper investigation or when stakes are high (news verification, journalistic investigation), continue with EXIF, multiple detectors, and detailed visual analysis.

Recommended tools to keep in your arsenal:

• Sensity (Reality Defender): Primary detector. Budget: $199-499/month for professional use
• InVID Extension: Quick reverse search. Cost: Free
• ExifTool: Professional EXIF analysis. Cost: Free
• TinEye: Alternative reverse search. Cost: Free with limitations; $200+/month professional
• Canva Pro: For editing and collaborative visual analysis. Cost: $13/month. Useful as complement

Behavioral recommendations:

1. Be skeptical of images generating extreme emotion: Images provoking extreme anger, fear, or amazement are frequently manipulation targets. Slow down, verify before sharing.

2. Always seek multiple sources: Real important events are reported by multiple publications. If only one image exists, it’s suspicious.

3. Understand it’s a cat-and-mouse game: As detection improves, generation improves. In 2027-2028, there will probably be AI images evading all 2026 detectors. The only defense is staying informed and critical.

4. Always mention when something is generated: If you work in media, social media, or communications, explicitly label generated images with “Created with Midjourney” or “Generated by DALL-E”. Transparency is fundamental.

Future perspective: In 2026, we’re at an inflection point. Generation technology is improving faster than detection. But infrastructure-level solutions are also emerging: Models can be trained to add imperceptible “digital signatures” proving authenticity. Adobe has proposed “Content Authenticity Initiative”. Google and other platforms are investing in labeling standards.

The future probably won’t be “images vs detection”, but “verifiable authenticity standards”. Until that’s widely implemented, the methods in this guide are your best defense.

🔗 Related Resources for Deeper Exploration: Since detecting AI-generated content is part of broader digital literacy, we also recommend:

• How to Use AI to Automate Freelancer Invoicing in 2026 (to understand legitimate AI use cases)
• How to Use Claude 3.5 for Data Analysis (for deeper analysis)

Your next step: Download ExifTool today. Practice with 10 real images and 10 generated ones (many available in r/midjourney). Within an hour, you’ll develop basic intuition. Within one week of regular analysis, you’ll be significantly better than the average user.

Frequently Asked Questions (FAQ)

What are the most obvious signs of an AI-generated image?

The most obvious signs include: (1) Deformed hands with incorrect finger count or impossible joints, (2) Illegible text on signs and labels, (3) Lighting inconsistencies where shadows don’t match the light source, (4) Odd-proportioned eyes or asymmetrical pupils, (5) Unnaturally blurry background that looks Photoshopped without real depth, (6) Excessive facial symmetry that seems “too perfect”, and (7) Completely absent EXIF metadata. In v1-v2 images from Midjourney and DALL-E, these errors were obvious. But with 2026’s v6+, they require detailed inspection.

Are there free tools to detect fake AI images?

Yes. Main free tools are: Google Images (reverse search), ExifTool (EXIF analysis), AI Image Detector by Hugging Face (open-source model based), InVID Browser Extension (social media verification), and Forensically (basic forensic analysis). Limitations: Hugging Face is better for Stable Diffusion than DALL-E. InVID is mainly for video. For professionals needing >90% accuracy, paid options like Sensity ($199+/month) are necessary. For casual/personal verification, free tools are 80% effective in most cases.

Why is it important to know if an image was created by Midjourney or DALL-E?

It’s important for several critical reasons: (1) Prevent misinformation: False images create fake news, deceptive political frames, and false testimony. (2) Personal protection: Deepfakes and face manipulation target extortion and identity theft. (3) Legal responsibility: In some countries, creating and distributing false images of public figures has legal consequences. (4) Journalistic integrity: Responsible media must verify image authenticity. (5) Commercial transparency: Using false images in advertising without disclosure is fraud. (6) Social media trust: As generated images increase, distinguishing reality from fiction is critical. Knowing the difference is fundamental digital survival skill.

How can people detect visual manipulation on social media?

Social media detection strategy: (1) Verify the source: Who posted this? Verified account with credible posting history, or new account without followers? (2) Search for context: Does multiple news coverage of the same event exist? Real events have multiple reporters. (3) Check verified comments: Do credible users confirm context? (4) Use reverse search: Right-click, search with Google Images. Does it appear in credible media? (5) Inspect details: Click for full-size view. Look for inconsistencies. (6) Verify timestamp: Does publication date match when the event occurred? “Now” photos months old are suspicious. In 2026, most social media visual misinformation follows these patterns, and combining these steps is 90%+ effective.

What differences exist between real and AI-generated images?

Fundamental differences: (1) Capture vs. Prediction: Real images capture actual photons. AI predicts pixels statistically. (2) Sensor noise: Real images have characteristic sensor noise in dark areas. AI generates smooth or artificial noise. (3) Metadata: Real images contain detailed EXIF data. AI typically doesn’t. (4) Coherent depth: Real images have actual optical depth with natural bokeh. AI often “fakes” depth. (5) Unpredictable complexity: Real images contain unpredictable details (specific pebble, unique shadow pattern). AI simplifies to “probable”. (6) Realistic anomalies: Real images can contain realistic anomalies (person with illness-caused arm deformity). AI rarely generates realistic anomalies; it produces perfection or obvious error, rarely “uncomfortable realism”. (7) Reverse search history: Real images have traceable history. AI usually doesn’t. Combined, these differences enable detection, though it becomes more challenging yearly.

Do AI images always have visible errors?

No. With DALL-E 3 v4+ and Midjourney v6+ from 2026, many images lack obvious errors to the untrained human eye. “Inanimate object” images (architecture, nature, objects without faces) can be nearly indistinguishable from real photos visually. However, they always have some differences if you know where to look: (1) Absent metadata, (2) Reverse search anomalies, (3) Subtle lighting inconsistencies, (4) Smoothed texture details. The error is assuming “if I see no obvious error, it must be real”. The truth is 2026 detection requires multiple methods, not just visual inspection. Most difficult cases require metadata access and AI detectors.

What’s the best tool for detecting deepfakes?

For deepfakes/face-swap specifically, Sensity (now Reality Defender) is currently the most reliable tool, with ~85-90% accuracy on modern deepfakes. For generated still images, detectors vary: Hugging Face works well for Stable Diffusion but less for DALL-E. Midjourney Official Detector is optimal for Midjourney images. Google’s built-in detection in Google Lens covers broad range but with ~75% accuracy. For professionals needing maximum accuracy, combine Sensity + Midjourney Detector + manual EXIF analysis. For casual users, Google Images + InVID Extension + visual observation is 80% effective.

How to identify AI-generated content?

Identifying AI content (texts, images, videos) requires different methods by type. For images: use methods described in this guide. For texts: (covered in our article on detecting articles written by Claude or Gemini), look for uniform syntax patterns, predictable structure, and lack of truly controversial viewpoints. For videos: same deepfake methods. Universal principle: AI content lacks the “chaotic information” real systems generate. AI tends toward statistical probability. Human and natural tend toward surprise.