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Easy, Quick, and Scalable Reverse Picture Search Utilizing Perceptual Hashes and DynamoDB | by Canva Engineering | Oct, 2022

Easy, Quick, and Scalable Reverse Picture Search Utilizing Perceptual Hashes and DynamoDB | by Canva Engineering | Oct, 2022

2022-10-16 19:03:38

Picture Hashing

by Christopher Bong

Header image for the blog post

Canva hosts an enormous assortment of ever-growing, person media that must be saved and managed correctly. The range of this media poses challenges across the moderation and discount of pointless duplicate content material. To cater to this, one instrument we use is perceptual hashing with an internally constructed reverse picture search system.

This weblog publish describes the design and course of we went by means of to construct such a system to the dimensions of Canva.

Hashing is one easy manner of implementing a reverse picture search. A hash operate is an algorithm that maps knowledge to distinctive fixed-sized values known as hash keys, that are represented as strings. Widespread hash algorithms embody MD5 and SHA.

The power to map any file into distinctive hash keys is beneficial for matching duplicate content material however just isn’t very viable within the case of pictures.

For example, you can implement a easy reverse picture search in a picture library utilizing the picture file hash key as a main key in a database.

Within the instance beneath, we now have two visually duplicated pictures (IMAGE_1 and IMAGE_2) whose SHA512 hash keys are the first keys in a DynamoDB desk, and their picture IDs are the secondary keys.

Hash keys using SHA512 for two visually duplicated images IMAGE_1 and IMAGE_2. Note that the hashes are completely different.
Hash keys utilizing SHA512 for 2 visually duplicated pictures IMAGE_1 and IMAGE_2

Despite the fact that the pictures are visually duplicated, it’s evident that their hash keys may be completely totally different, even when there’s a single pixel of distinction.

In a picture library, a number of pictures are visually comparable however can have minor high quality or metadata modifications. All of those pictures may have totally different hash keys. Therefore, a reverse picture search utilizing easy hashing is just helpful within the context of completely duplicated pictures.

Two photos of sushi side by side with their MD5 hashes below each. The right photo has been modified with a watermark.
A picture with a small watermark utilized to it, fully altering its hash

Whereas cryptographic hashes like SHA512 create hashes of uncooked bytes, perceptual hashes are created based mostly on the precise pixels of a picture. Perceptual hashes change together with the visible change within the pictures. Therefore, a reverse picture search utilizing perceptual hashing may be executed by calculating the Hamming distance between the 2 hashes.

Perceptual hashing algorithms can use numerous transformations and a few are more practical for matching totally different picture sorts like images or vectors. For this weblog publish, we’ll use for instance the pHash algorithm because it produces shorter hashes which can be simpler to make use of in examples.

Two photos of sushi side by side with their PDQ hashes below each. The right photo has been modified with a watermark.

The unique picture above is modified with a watermark. Because it’s a minimal modification, there’s solely a small Hamming distance of two between them. With extra modifications, there can be a bigger Hamming distance. If there have been no modifications, their hashes would have a Hamming distance of 0. Perceptual hashes and Hamming distances can be utilized to construct a system that may discover modified pictures.

A perceptual hash matching system can use a hash and a Hamming distance as enter. The system output can be all of the saved hashes inside that Hamming distance. The earlier instance can’t be used to create this method because it solely supported matching if hashes are equal. Permitting this question kind wants a distinct knowledge construction. As well as, the system ought to have the correct stage of precision and recall. The best is to have increased precision and keep away from false negatives.

To do that we carried out an algorithm known as multi-index hashing (Norouzi, 2012) on high of DynamoDB tables. The algorithm entails splitting the question hash and saved hashes into segments (extra on this later). This resolution was chosen as a result of it is ready to assure discovering hashes for a given Hamming distance at excessive scale. Right here’s an instance of what the entries within the database may appear like for a perceptual hash:

A table with entries of a DynamoDB table. There are four rows and three column. The first column is partition key of each being one part of the split hash. The second is the hashed image. The third is the row number.

The hash is cut up into 4 components, with every key prefixed with the index of that half. The identical cut up is completed on the picture at question time to match. The picture id is the kind key for uniqueness between duplicates.

A diagram of a query hash being split into 4 DynamoDB queries.

With the 4 hashes, 4 separate DynamoDB queries are run. Then, a consolidation step is run on the outcomes to take away duplicates and filter out hashes with a Hamming distance increased than the edge.

This resolution can discover comparable hashes with the runtime of a DynamoDB GetItem question. Nevertheless, its effectiveness relies on the hash cut up depend and the character of the pictures. Specifically, the hash cut up depend was one of many vital choices that needed to be made and was why the implementation was unusable at rollout.

Within the above instance, the hash is cut up into 4 components. With a 4 cut up, it’s assured that each one outcomes with a Hamming distance of three or much less may be discovered. That is because of the pigeonhole principle. For instance, if there are n slots that the hash is cut up into, and n — 1 character modifications to distribute, it’s assured that no less than one of many slots has no characters modified.

A diagram of a hash being split into 4 windows and 3 modifications represented as dots. Each dot has an arrow pointing to a separate window.

That is how there’s a assure of discovering all outcomes inside a Hamming distance threshold. Naturally, one may choose a excessive slot depend for supporting as giant a variety as doable. Nevertheless, growing this quantity had two vital implications for the outcomes:

  1. The outcome set elevated, and
  2. The variety of matches with excessive Hamming distances elevated.

Instantly after rollout, there have been points that brought on the system to be unusable. Queries took minutes to run, used 20x extra learn capability than anticipated, and returned only a few outcomes. DynamoDB queries have been returning an extreme quantity of outcomes that have been all filtered out within the consolidation step.

Investigating the queries and their outcomes revealed two fascinating insights:

See Also

  • For one, it was widespread for customers to add duplicates of the identical picture. This meant the individuality distribution for pictures was not as distinctive as initially anticipated, resulting in extra outcomes per search.
A cropped screenshot of the Canva uploads page where 5 of the images are the same but slightly modified.
A pattern of what my (and doubtless many different customers) uploads appear like in Canva
  • The opposite drawback concerned the hashing algorithm. The perceptual hash getting used produces pretty uniform hashes for non-complex pictures like single-colored form vectors (i.e., AAAAAAAA). This meant that many vectors in our database had the identical partition keys. If any question hash matched a slot with these uniform hashes, 100000s of vector hashes can be returned within the outcome set.
Colored boxes that inflated our search results to the point of unusability. Three colored boxes are shown with slightly different dimensions and completely different colors. From top clockwise: a green box, a yellow box and a light pink box.
Coloured containers that inflated our search outcomes to the purpose of unusability

To repair these points, two modifications have been made:

  • First, the window depend was decreased 4x to scale back the variety of outcomes that the system filtered. After every discount, exams have been executed to make sure that no related picture matches have been misplaced.
  • Then we selected to skip hashing low-complexity pictures. These have been simple to select as a result of the hashes typically consisted of a single character like AAAAAAAA.

These modifications decreased the variety of rows returned from 100000s to 10s for all of the beforehand problematic queries, fixing the problems that occurred on roll-out.

A number of the highlights of the system are:

  1. Hashes for over 10 billion pictures in DynamoDB.
  2. Common question time of 40ms, p95 at 60ms.
  3. Our peak workload is in extra of 2000 queries per second, with no efficiency degradation at that utilization, which maps to ~10k DynamoDB RCU capability.
  4. Catches watermarking, high quality modifications, and minor modifications.

We’ve already been leveraging the system for numerous use circumstances at Canva. One use case comes from the sheer quantity of media that’s saved which is similar to most giant media-based tech corporations. This comes with a big price within the type of storage, with deduplication of bytes as a method to scale back this.

Since we now have a picture hash desk, we will analyze the variety of distinctive hashes and decide the deduplication share of all our pictures. This will even be prolonged to figuring out “comparable” pictures as properly, discovering these situations of sunshine modification.

For content material moderation, a system working on perceptual hashes offers us the flexibility to rapidly act on recognized unlawful, graphic, or harmful media throughout our dataset. Because of this we will motion media takedowns at scale in a matter of seconds with tooling, moderately than guide intervention.

Perceptual hashing is just one instrument within the toolkit for managing a big media footprint. Its performance is just simply being explored at Canva and we stay up for bettering hit charges in opposition to different transforms reminiscent of pictures inside pictures.


Big because of everybody within the media platform group for serving to to get this venture by means of. Particular because of:

  • Jacky for teaching and supporting me all through the venture
  • Oliver for serving to with the implementation and for co-owning the service

Michael and Raymond for offering their safety perspective and experience.

Wish to work on scaling media storage and entry at Canva? Join us!

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