What is dHash (Difference Hash) and how does this dHash generator work?

dHash, or Difference Hash, is a perceptual hashing algorithm. Our dHash generator resizes the image to a small grayscale square (e.g., 9x8 pixels for an 8x8 hash). It then compares the brightness of adjacent pixels horizontally. If a pixel is brighter than its right neighbor, it's a '1'; otherwise, it's a '0'. This sequence of bits forms the dHash. It's good at detecting similar images despite scaling and minor color/brightness adjustments.

What is aHash (Average Hash) and how does this aHash generator work?

aHash, or Average Hash, is another perceptual hashing algorithm. Our aHash generator resizes the image to a small square (e.g., 8x8 pixels), converts it to grayscale, and calculates the average grayscale value of all pixels. Then, each pixel is compared to this average: if it's brighter or equal, it's a '1'; otherwise, it's a '0'. These bits form the aHash. It's simple and fast.

Does this tool generate pHash, Wavelet Hash, or Color Hash?

Currently, this online image hashing tool specializes in generating dHash and aHash. More complex perceptual hashes like pHash (Perceptual Hash, often DCT-based), Wavelet Hash, or Color Hash (which considers color information more directly) are not implemented in this specific version. These often require more intensive computation, typically found in libraries like ImageHash for Python or OpenCV.

Are these hashes crop-resistant?

dHash and aHash are generally not very crop-resistant. Significant cropping will likely result in a very different hash. Specialized crop-resistant hashing algorithms exist but are more complex and not part of this tool.

How can I use the generated hashes for image duplicate detection or finding similar images?

Once you have hashes for a collection of images, you compare them pairwise. The Hamming distance (number of differing bits) between two hashes indicates similarity. A distance of 0 means they are likely identical or extremely similar. A small distance (e.g., 1-5 for a 64-bit hash, depending on sensitivity needs) suggests high visual similarity. This tool generates the hashes; comparison logic needs to be implemented separately if you're managing a large dataset.

Can I use this tool for batch image hashing or processing large datasets?

This tool is designed for generating hashes for individual images interactively in the browser. It does not currently support batch image hashing for multiple files at once or processing very large datasets directly. For such tasks, scripting with libraries like Python's ImageHash or tools designed for bulk processing would be more suitable.

Are my images uploaded to a server for hash generation?

No, this image hash generator performs all computations directly in your web browser using JavaScript and the HTML Canvas API. Your images are not uploaded to any external server, ensuring your privacy and security.