SIX ECHO
Reflection of creativity
The current situation on intellectual property
With the size and growth of market on digital's contents, there is one big problem; its identity and chain of ownership, this is known as provenance
As seen on diagram below which is showing the growth of wealth in the next decade on the market, more assets mean more intellectual property infringement
Source: Deloitte Luxembourg & ArtTactic Art & Finance Report 2017
As mentioned above, we foresee infinite problems that arise. SIX Network has started the ECHO project in order to help creators of digital assets in various forms. Whether it's photos, articles, novels, musics and videos, able to bring their own digital assets to report/record ownership rights. Including being able to help those who want to publish, reproduce or distribute to verify the rights to assets that the assets acquired that is a legitimate asset without violating the rights of the creators
Our goals
To give digital content creators to store their works in our SIX digital asset storage chain which would be stored on the blockchain
With the proof of ownership and proof of existence, such works could be easily tracked from the first hash since the beginning
With the power of blockchain, all related parties would be collaborate and revenue sharing through agreements on smart contracts
To facilitate wallet-to-wallet decentralized between the creators and consumers
We are going to make a better future for the whole digital economy and the digital creative industries
Our solutions
ECHO will store asset's identity and ownership history in a secure digital token/smart contract
Semantic metadata
Assets' information such as title, author, creator, publisher, photographer, etc.
Digital fingerprint
The set of binary digits that uniquely identifies an digital asset
ECHO will not store contents in blockchain but semantic data and digital fingerprint
ECHO uses unique consensus method to synchronized asset's identity
ECHO creates index of creative works which will allow creators to explore their rights
ECHO project is an open source under the term of Apache-2.0
ECHO uses SIX token as a fee to store and verify creative works
ECHO overall
Our technic
Digital fingerprint
Generate contents' digest by mathematical algorithm which its result will be a set of binary arrays
Validators
By checking the duplication of;
Semantic metadata
Digest hash
Consensus model
Together with our platform's partners, SIX Storage Chain will manage and handle the consensus protocol as governance core chain
Semantic metadata and digital fingerprint will also be published to other public chains but not limited to, such as Klaytn, Ethereum, etc.
In practice
ECHO provides an SDK for a platform, like OOKBEE, to install on their environment to communicate to our SIX Storage Chain environment
SDK functions;
Semantic metadata input
Digital fingerprint generation
ECHO does not only limited to novel and article but also all related digital contents creator businesses/industries, such as music industries, production studios, advertisement houses, etc.
Format of digital contents on ECHO
Sound/Music
Image/Photograph
Text(Novel)
ECHO technical process
Our technic on how ECHO check and validate the duplication of IP as described here below
Image digest
First, we are going to create four digest formats
aHash, average hash value of the image digest
Reduce the size, by doing this, the high frequencies/detail of the image will be reduced and removed by shrinking the image size together with ANTIALIAS function(to still remain the detail as much as possible). Now the size of the image will be 8x8 which is 64 pixels in total
Reduce the colors, from RGB which is 64x64x64 to be in grayscale which will be on 64 colors in total
Compute the average of each pixel
[pixels] = numpy.asarray(image)
avg = pixels.mean()
Now we have the image's mean value in arrays
Construct the bits' array
Compare each of pixels' array by higher or lower than the average and record them into the pixels' array​
Construct the hash
By big-endian(left -> right, top -> bottom), we are going to get 64 bit integers
pHash, perceptive hash value of the image digest
Reduce the size, by doing this, the high frequencies/detail of the image will be reduced and removed by shrinking the image size together with ANTIALIAS function(to still remain the detail as much as possible). Now the size of the image will be 8x8 which is 64 pixels in total
Reduce the colors, from RGB which is 64x64x64 to be in grayscale which will be on 64 colors in total
Compute DCT; Discrete Cosine Transform, by DCT technique, the correction/modification of the color histogram and gamma will not lead the digest to be false-misses. With this technique, even we get the image which changing the gamma and histogram but the average value of the pixels will not be dramatically shifted from the original image
Compute the average of each pixel
[pixels] = numpy.asarray(image)
avg = pixels.mean()
Now we have the image's mean value in arrays​
Construct the bits' array
Compare each of pixels' array bu higher or lower than the average by putting 1 as above average and 0 as below average in each of pixels' array
By 0,1 value on each array, this will also lead to the ignorance of the correction/modification of the histogram and gamma​
Construct the hash
By big-endian(left -> right, top -> bottom), we are going to get 64 bit integers
dHash, gradient difference hash value of the image digest
Reduce the size, by doing this, the high frequencies/detail of the image will be reduced and removed by shrinking the image size together with ANTIALIAS function(to still remain the detail as much as possible). Now the size of the image will be 8x8 which is 64 pixels in total
Reduce the colors, from RGB which is 64x64x64 to be in grayscale which will be on 64 colors in total
Compute the gradient between adjacent pixels and record its array trend
Construct the hash
By big-endian(left -> right, top -> bottom), we are going to get 64 bit integers
wHash, discrete wavelet transform hash value of the image digest
Reduce the size, by doing this, the high frequencies/detail of the image will be reduced and removed by shrinking the image size together with ANTIALIAS function(to still remain the detail as much as possible). Now the size of the image will be 8x8 which is 64 pixels in total
Reduce the colors, from RGB which is 64x64x64 to be in grayscale which will be on 64 colors in total
Compute DWT; Discrete Wavelet Transform, by DWT technique, the image's pixels are going to be stored in [-1,1] which will be in -1,0,1 values by this values the image frequencies will be scales and shifted to be in square-shaped form
Compute the average of each pixel
[pixels] = numpy.asarray(image)
avg = pixels.mean()
Now we have the image's mean value in arrays
Construct the bits' array
Compare each of pixels' array by higher or lower than the average and record them into the pixels' array
Construct the hash
By big-endian(left -> right, top -> bottom), we are going to get 64 bit integers
Text digest
First, create 3 digest per one text content(part of Natural Language Processing;NLP) then with the Jaccard similarity index to check the duplication
Word tokenization, we are going to divide the text content to be a smaller parts call tokens and each of the token is coming from the comparison with the dictionary(both Thai and English)
Create three group of tokens by:
The array of group 25 tokens to represent 50% of the content
The array of group 14 tokens to represent 70% of the content
The array of group 9 tokens to represent 80% of the content
Generate the hash of each group to represent its group of digest
Those three hash together will be represented the content fingerprint
To check the duplication of the text content, we are using Jaccard similarity index technique, the formula of the technique is:
By above formula, in our case will be:
Count the numbers of group 25 tokens which are shared between each content
Count the total numbers of both contents
Divide the number of shared by the total number of both contents
Multiply by 100 will be the percentage of how similar of these two contents
If it is less than 50%, then we will consider that it is not the same content
If it is more than 50% then we will have a further check
Now we are going to check with the 14 tokens group
If it is less than 70% then we will consider that it is not the same content
If it is more than 70% then we will check with the last group of tokens which is group of 9 tokens
If the check result is less than 80% then we will consider that it is good to go
If the check result is more than 80% then we will reject the content as a duplication check failure
SIX Storage Chain
Now with digest information, we are going to prepare them in our SSC's smartcontract format and submit them into SSC
Once, we get the new image from ECHO's SDK which is going to record its IP into our SSC then:-
Verify the duplication by submit its digest information into SSC
SSC, SIX Storage Chain, thanks to the EOS which is allowing us to construct not from scratch our SIX Storage Chain, with the pros of the chain, we could easily build our Delegated Proof-of-Stake and also with the operations or services can be done without any associated costs but the CPUs and Rams.
By the SSC validator's nodes, they will check the duplication of the digest information by comparing the existing digest information on the chain by 4 hash(aHash, pHash, dHash and wHash) of the image or 3 hash for the text
Once the digest is good to submit, then the chosen verifier node will submit into the SSC
The remaining nodes will verify its correctness
ECHO on public chain
ECHO transaction ID and block number
With the information of our ECHO transaction and block number of the asset, we are now going to publish its information into the public chains but not limited to, such as Klaytn and Ethereum
By these two data on the public chain, we could check all of the digital asset information in our ECHO.WORK which is now in the selected user test, it will be public release soon(Q1, 2020)
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