Bash duplicate md5 finder3/9/2023 ![]() Then, based on these differences, a hash value is created. Simply put, the dHash algorithm looks at the difference between adjacent pixel values. In particular, we’ll be using the “difference hash”, or simply dHash algorithm to compute our image fingerprints. Using image hashing algorithms makes performing near-duplicate image detection substantially easier. Images that are “similar” should have hashes that are “similar” as well”. Given an input image, we are going apply a hash function and compute an “image hash” based on the image’s visual appearance. Image hashing is the process of examining the contents of an image and then constructing a value that uniquely identifies an image based on these contents.įor example, take a look at the image at the top of this post. Remove ads What is image fingerprinting/hashing? This technique is commonly called “perceptual image hashing” or simply “image hashing”. We are going to utilize image fingerprinting to perform near-duplicate image detection. Years later, I want to share the basics of this algorithm with you, in hopes that you can use it in your own projects.īut the biggest question is, how do we create this image fingerprint? Even a few of the investors were left kicking rocks.īut one thing did survive. They continued to fund us - until the recession hit. We also managed to keep the investors happy. It never fully stopped - but by using this algorithm we managed to reduce the number of inappropriate uploads by over 80%. ![]() As image moderators flagged new pornographic images, they were also fingerprinted and stored in our database, creating an ever-evolving database that could be used to prevent invalid uploads.If the fingerprint of the upload matched the fingerprint of any inappropriate images, we prevented the user from setting the image as their profile picture. When a user uploaded a new profile picture, we compared it to our database of image fingerprints.Fingerprint our set of inappropriate images and store the image fingerprints in a database.This lead to the implementation of a three stage algorithm: And just like a fingerprint can identify a person, it can also identify an image. Indeed, the tides of filth were beating against the ivory tower, threatening to topple it into the sea.Īs the knees of the dating giant started to buckle, I proposed a more robust and long-term solution: What if we used image fingerprinting to combat the outbreak? Worse yet, investors were threatening to pull their funding if the company did not solve the problem. For the first time in eight months, user ship failed to double (and had even started to decline). The outbreak reached critical levels in July of 2009. They were literally throwing tens of thousands of dollars (not to mention, countless man-hours) at the problem, simply trying to moderate and contain the outbreak rather than stop it at its source. They hired teams of image moderators who did literally nothing but stare at an administration page for 8+ hours a day and remove any new pornographic images that were uploaded to the social network. In short, they had a very real venture-backed reputation that they needed to uphold.ĭesperately, the dating website scrambled to stop the porn outbreak. Remember, this was a startup that had marketed themselves as a superior dating-haven, free of the filth and trash that plagued other dating websites. However, this behavior could be neither accepted nor ignored. ![]() Or it may even be considered “normal” or “expected”, just a by-product of online dating that was simply accepted and ignored. Now, perhaps for some dating websites a few pornographic images here-and-there may not be a problem. This behavior ruined the experience for many of their customers - leading to them canceling their memberships. Things were looking extremely good for them.īut they had a serious problem - a porn problem.Ī small subset of the dating website’s users were uploading pornographic images and setting them as their profile pictures. These early successes lead to the coveted exponential growth in startups - their user ship was doubling each month. And they were even featured in a short spotlight on national television. This wasn’t a site where you would go for hookups - this is where you went to find an honest relationship.įueled by millions in venture capital (this was prior to the recession in the United States), their online advertisements regarding true love and finding a soulmate converted like gangbusters. Unlike other dating websites, this company marketed themselves with a squeaky clean reputation. They were an early stage startup, but were starting to see some initial traction.
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