Image Clone Detector
Upload an image to detect copy-move forgeries using our free online Image Clone Detector. The algorithm divides your image into overlapping pixel blocks, computes color and texture signatures, and finds cloned/copied regions using similarity matching. View detected clone regions on a heatmap overlay, see confidence scores, and adjust sensitivity for thorough forensic analysis. All processing is done entirely in your browser - no uploads, no signup required.
Upload an image to detect copy-move forgeries using pixel-block matching. The algorithm analyzes overlapping blocks, finds similar regions, and generates a heatmap overlay showing potential cloned areas. Adjust the sensitivity to fine-tune detection - all processing is done entirely in your browser.
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Supports JPEG, PNG, WebP, BMP, GIF, TIFF - max 4096×4096
The copy-move forgery detection algorithm divides the image into overlapping pixel blocks, computes a color + texture signature for each block, and compares all block pairs using cosine similarity. Blocks that are very similar but located in different parts of the image are flagged as potential clones. Connected similar blocks are clustered into regions and shown as a heatmap overlay. This technique detects common image manipulation where objects are cloned/copied to duplicate or hide elements in the scene.
Why Use Our Image Clone Detector?
Copy-Move Forgery Detection
Detects cloned or copied regions within any image using pixel-block matching. The algorithm divides the image into overlapping blocks, computes color and texture signatures for each block, and finds similar pairs across different image regions. Perfect for identifying common image manipulation techniques where objects are duplicated to hide or alter scene content.
Adjustable Sensitivity Settings
Fine-tune detection with three sensitivity presets. High sensitivity detects even subtle cloning patterns (ideal for detailed forensic analysis). Medium provides balanced detection for general use. Low sensitivity reduces false positives by requiring stronger similarity matches. Each preset adjusts block size, step size, similarity threshold, and minimum region size.
Visual Heatmap & Region Details
Results are displayed as a color-coded heatmap overlay showing the location and intensity of detected clone regions. Each detected region is listed with its bounding box coordinates, dimensions, and similarity score. The heatmap uses red/hot colors to indicate areas with the strongest clone indicators.
100% Private Browser-Based Processing
All image analysis happens entirely in your browser using the Canvas API. Images are read via the FileReader API and processed locally - no data is ever uploaded to any server. Your images stay completely private on your device. No signup, no account, no data collection.
Common Use Cases for Image Clone Detector
Forensic Evidence Verification
Verify the authenticity of photographic evidence used in legal proceedings. Detect if images submitted as evidence contain cloned regions that may indicate manipulation. Journalists, investigators, and legal professionals can use our Image Clone Detector to screen images before presenting them as evidence in court or publishing them in news reports.
Fact-Checking & Misinformation Detection
Screen images circulating on social media and news platforms for signs of copy-move forgery. Fact-checkers and journalists can quickly verify whether images have been manipulated by detecting duplicated regions. This helps identify doctored images used in disinformation campaigns, fake news, and misleading social media posts.
Insurance Fraud Investigation
Detect fraudulent claims where images of damage, accidents, or theft have been manipulated. Investigators can scan claim photos for cloned regions that may indicate photoshopping. Our tool helps identify manipulated evidence in insurance fraud cases involving property damage, vehicle accidents, and personal injury claims.
Academic & Research Integrity
Verify the authenticity of images used in academic papers and research publications. Detect image manipulation in scientific figures, particularly in life sciences and medical imaging where copy-move forgery has been used to falsify results. Ensure research integrity by screening submitted images for tampering.
Social Media Image Verification
Verify the authenticity of images shared on social media platforms. Detect clone-stamp and content-aware fill manipulations common in photo editing. Content moderators, journalists, and concerned users can screen viral images for manipulation before sharing or reporting on them.
Digital Forensics Education
Learn about image forensics and copy-move forgery detection techniques. Students and educators can use our tool to understand how block-matching algorithms work, explore the effect of sensitivity settings on detection accuracy, and study common image manipulation techniques used in digital forensics.
Understanding Copy-Move Forgery Detection
What is Copy-Move Forgery?
Copy-move forgery is one of the most common image manipulation techniques where a portion of an image is copied and pasted (cloned) to another location within the same image. This technique is often used to duplicate objects (making a scene appear more crowded), hide unwanted elements (covering an object by cloning nearby background), or create misleading composites. Unlike splicing (combining two different images), copy-move forgery uses pixels from the same image, making it harder to detect because the color, lighting, noise, and camera characteristics match perfectly with the source region.
How Our Block-Matching Algorithm Works
- Block Division: The image is divided into many small overlapping square blocks (typically 12-24 pixels on each side). Overlapping ensures that cloned regions are detected regardless of their alignment within the grid.
- Signature Computation: For each block, a 6-dimensional signature is computed consisting of the average RGB color values plus the standard deviation of each channel. This captures both the color and the texture/contrast of the block, making it robust against minor compression artifacts.
- Similarity Matching: Every block pair is compared using cosine similarity of their signatures. Blocks that are very similar (above the threshold) but located in different parts of the image are flagged as potential clone pairs. Neighboring blocks are excluded to avoid detecting natural texture repetition.
- Region Clustering: Flagged block pairs are clustered into connected regions using a union-find algorithm. Regions that contain enough blocks (based on the minimum region size setting) are reported as detected clone regions with bounding boxes and similarity scores.
When to Use This Tool vs. Other Forensic Tools
- Use the Image Clone Detector for: Detecting copy-move forgery (cloning/stamping within the same image), verifying image authenticity in journalism and legal contexts, screening images for manipulation, learning about digital forensics, and preliminary forensic analysis.
- Use complementary tools for: Error Level Analysis (ELA) to detect image regions with different compression levels, metadata analysis to check for unusual editing history, steganography detection for hidden data, and image integrity checking for re-compression detection.
- Limitations: This algorithm detects pixel-level duplication. It may not detect cloning performed with anti-forensic techniques (e.g., slight scaling, rotation, or color adjustments to cloned regions). Very small cloned regions (under ~48×48 pixels at medium sensitivity) may be missed.
Privacy, Security & Limitations
Our Image Clone Detector processes everything locally in your browser. Images are read using the FileReader API and analyzed using the Canvas API - no data is ever sent to any server. The tool is 100% free with no signup, no account, and no usage limits. All processing is done entirely on your device.
Important limitations: The algorithm is designed for detecting exact or near-exact copy-move forgeries. It may produce false positives on images with repetitive natural patterns (e.g., brick walls, grass, fabrics) or false negatives on images where cloned regions have been transformed (scaled, rotated, or color-adjusted). For images larger than 4096×4096 pixels, resize before uploading. Processing time depends on image size and sensitivity settings - larger images with high sensitivity may take several seconds.
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Frequently Asked Questions About Image Clone Detector
Copy-move forgery detection is a digital image forensic technique that identifies regions within an image that have been copied and pasted (cloned) to another location in the same image. The algorithm works by dividing the image into overlapping pixel blocks, computing signatures for each block based on color and texture, and finding pairs of blocks that are very similar but located in different parts of the image. This technique is commonly used to verify image authenticity in journalism, legal proceedings, and scientific research.
Our Image Clone Detector is specifically designed for copy-move forgery - where a region is cloned from one part of an image to another. This includes: object duplication (making a scene appear more crowded), object removal (cloning background to cover something), and region cloning (duplicating textures or patterns). The tool is most effective for exact or near-exact copies. It may not detect manipulations where the cloned region has been significantly scaled, rotated, or color-adjusted.
The tool supports all common image formats: JPEG/JPG, PNG, WebP, BMP, GIF, TIFF, and AVIF. The maximum supported resolution is 4096×4096 pixels. For larger images, please resize before uploading. All processing is done client-side in your browser using the Canvas API - images are never uploaded to any server.
Three sensitivity presets adjust the detection algorithm: High (block size 12px, step 6px, threshold 88%) detects even subtle cloning patterns but may increase false positives on textured images. Medium (16px blocks, 8px step, 92% threshold) provides balanced detection for most use cases. Low (24px blocks, 12px step, 95% threshold) is conservative, reducing false positives but potentially missing subtle cloning. Higher sensitivity is better for detailed forensic analysis while lower sensitivity is better for quick screening.
The accuracy depends on several factors: the size of the cloned region (larger regions are easier to detect), the image resolution (higher resolution provides more detail), the presence of repetitive patterns (which can cause false positives), and whether the cloned region was modified after cloning (scaling, rotation, or color adjustments reduce detectability). Our tool provides a confidence score and confidence label to help interpret results. For critical applications, results should be verified using additional forensic techniques.
Our Image Clone Detector is designed specifically for copy-move forgery detection within a single image. It is not designed for detecting AI-generated images (which are created entirely by AI) or deepfakes (which typically involve face swapping in video). For AI-generated image detection, look for artifacts like inconsistent lighting, unnatural hand/finger anatomy, strange backgrounds, and watermark artifacts. Our tool may help detect if an AI-generated image had cloned post-processing artifacts, but it is not a general AI detection tool.
Absolutely. All image processing happens entirely within your browser using the Canvas API and FileReader API. Images are read locally and never uploaded to any server. Your images and detection results stay completely private on your device. No signup, no account, no data collection, no usage tracking related to your images. You can verify this by using the browser developer tools to check that no network requests are made when processing images.
Yes! 100% free with no signup, no account, no API key, and no usage limits. Analyze as many images as you need - completely free, forever. All processing happens in your browser with no server costs. No hidden charges, no premium tiers, no ads for the tool itself.