iscc-core 1.2.1

ISCC - Core Algorithms

ISCC - Codec & Algorithms

Create similarity-preserving identifiers for digital content

iscc-core is the reference implementation of the core algorithms of ISO 24138 – ISCC (International Standard Content Code)

Key Features

• Similarity-Preserving: Detect similar content even after modifications
• Multi-Level Identification: Identify content at metadata, perceptual, and data levels
• Self-Describing: Each component contains its own type and version information
• ISO Standardized: Implements the official ISO 24138:2024 specification
• Highly Tested: 100% test coverage with conformance test vectors

What is the ISCC

The ISCC is a similarity preserving fingerprint and identifier for digital media assets.

ISCCs are generated algorithmically from digital content, just like cryptographic hashes. However, instead of using a single cryptographic hash function to identify data only, the ISCC uses various algorithms to create a composite identifier that exhibits similarity-preserving properties (soft hash).

The component-based structure of the ISCC identifies content at multiple levels of abstraction. Each component is self-describing, modular, and can be used separately or with others to aid in various content identification tasks. The algorithmic design supports content deduplication, database synchronization, indexing, integrity verification, timestamping, versioning, data provenance, similarity clustering, anomaly detection, usage tracking, allocation of royalties, fact-checking and general digital asset management use-cases.

What is iscc-core

iscc-core is the python based reference implementation of the ISCC core algorithms as defined by ISO 24138. It is also a good reference for porting ISCC to other programming languages.

!!! tip This is a low level reference implementation that does not inlcude features like mediatype detection, metadata extraction or file format specific content extraction. Please have a look at iscc-sdk which adds those higher level features on top of the iscc-core library.

Implementors Guide

Reproducible Environment

For reproducible installation of the reference implementation we included a poetry.lock file with pinned dependencies. Install them using Python Poetry with the command poetry install in the root folder.

Repository structure

iscc-core
├── docs       # Markdown and other assets for mkdocs documentation
├── examples   # Example scripts using the reference code
├── iscc_core  # Actual source code of the reference implementation
├── tests      # Tests for the reference implementation
└── tools      # Development tools

Testing & Conformance

The reference implementation comes with 100% test coverage. To run the conformance selftest from the repository root use poetry run python -m iscc_core. To run the complete test suite use poetry run pytest.

To build a conformant implementation work through the follwing top level entrypoint functions:

gen_meta_code_v0
gen_text_code_v0
gen_image_code_v0
gen_audio_code_v0
gen_video_code_v0
gen_mixed_code_v0
gen_data_code_v0
gen_instance_code_v0
gen_iscc_code_v0

The corresponding test vectors can be found in iscc_core/data.json.

ISCC Architecture

ISCC MainTypes

Idx	Slug	Bits	Purpose
0	META	0000	Match on metadata similarity
1	SEMANTIC	0001	Match on semantic content similarity
2	CONTENT	0010	Match on perceptual content similarity
3	DATA	0011	Match on data similarity
4	INSTANCE	0100	Match on data identity
5	ISCC	0101	Composite of two or more components with common header

Installation

Use the package manager pip to install iscc-core as a library.

pip install iscc-core

Quick Start

import json
import iscc_core as ic

meta_code = ic.gen_meta_code(name="ISCC Test Document!")

print(f"Meta-Code:     {meta_code['iscc']}")
print(f"Structure:     {ic.iscc_explain(meta_code['iscc'])}\n")

# Extract text from file
with open("demo.txt", "rt", encoding="utf-8") as stream:
    text = stream.read()
    text_code = ic.gen_text_code_v0(text)
    print(f"Text-Code:     {text_code['iscc']}")
    print(f"Structure:     {ic.iscc_explain(text_code['iscc'])}\n")

# Process raw bytes of textfile
with open("demo.txt", "rb") as stream:
    data_code = ic.gen_data_code(stream)
    print(f"Data-Code:     {data_code['iscc']}")
    print(f"Structure:     {ic.iscc_explain(data_code['iscc'])}\n")

    stream.seek(0)
    instance_code = ic.gen_instance_code(stream)
    print(f"Instance-Code: {instance_code['iscc']}")
    print(f"Structure:     {ic.iscc_explain(instance_code['iscc'])}\n")

# Combine ISCC-UNITs into ISCC-CODE
iscc_code = ic.gen_iscc_code(
    (meta_code["iscc"], text_code["iscc"], data_code["iscc"], instance_code["iscc"])
)

# Create convenience `Code` object from ISCC string
iscc_obj = ic.Code(iscc_code["iscc"])
print(f"ISCC-CODE:     {ic.iscc_normalize(iscc_obj.code)}")
print(f"Structure:     {iscc_obj.explain}")
print(f"Multiformat:   {iscc_obj.mf_base32}\n")

# Compare with changed ISCC-CODE:
new_dc, new_ic = ic.Code.rnd(mt=ic.MT.DATA), ic.Code.rnd(mt=ic.MT.INSTANCE)
new_iscc = ic.gen_iscc_code((meta_code["iscc"], text_code["iscc"], new_dc.uri, new_ic.uri))
print(f"Compare ISCC-CODES:\n{iscc_obj.uri}\n{new_iscc['iscc']}")
print(json.dumps(ic.iscc_compare(iscc_obj.code, new_iscc["iscc"]), indent=2))

The output of this example is as follows:

Meta-Code:     ISCC:AAAT4EBWK27737D2
Structure:     META-NONE-V0-64-3e103656bffdfc7a

Text-Code:     ISCC:EAAQMBEYQF6457DP
Structure:     CONTENT-TEXT-V0-64-060498817dcefc6f

Data-Code:     ISCC:GAA7UJMLDXHPPENG
Structure:     DATA-NONE-V0-64-fa258b1dcef791a6

Instance-Code: ISCC:IAA3Y7HR2FEZCU4N
Structure:     INSTANCE-NONE-V0-64-bc7cf1d14991538d

ISCC-CODE:     ISCC:KACT4EBWK27737D2AYCJRAL5Z36G76RFRMO4554RU26HZ4ORJGIVHDI
Structure:     ISCC-TEXT-V0-MCDI-3e103656bffdfc7a060498817dcefc6ffa258b1dcef791a6bc7cf1d14991538d
Multiformat:   bzqavabj6ca3fnp757r5ambeyqf6457dp7isywhoo66i2npd46hiutektru

Compare ISCC-CODES:
ISCC:KACT4EBWK27737D2AYCJRAL5Z36G76RFRMO4554RU26HZ4ORJGIVHDI
ISCC:KACT4EBWK27737D2AYCJRAL5Z36G7Y7HA2BMECKMVRBEQXR2BJOS6NA
{
  "meta_dist": 0,
  "content_dist": 0,
  "data_dist": 33,
  "instance_match": false
}

Documentation

Documentation is published at https://core.iscc.codes

Development

Requirements

• Python 3.9 or higher for code generation and static site building.
• Poetry for installation and dependency management.

Development Setup

git clone https://github.com/iscc/iscc-core.git
cd iscc-core
poetry install

Development Tasks

Tests, coverage, code formatting and other tasks can be run with the poe command:

poe

Poe the Poet - A task runner that works well with poetry.
version 0.18.1

Result: No task specified.

USAGE
  poe [-h] [-v | -q] [--root PATH] [--ansi | --no-ansi] task [task arguments]

GLOBAL OPTIONS
  -h, --help     Show this help page and exit
  --version      Print the version and exit
  -v, --verbose  Increase command output (repeatable)
  -q, --quiet    Decrease command output (repeatable)
  -d, --dry-run  Print the task contents but don't actually run it
  --root PATH    Specify where to find the pyproject.toml
  --ansi         Force enable ANSI output
  --no-ansi      Force disable ANSI output
CONFIGURED TASKS
  gentests       Generate conformance test data
  format         Code style formating with black
  docs           Copy README.md to /docs
  format-md      Markdown formating with mdformat
  lf             Convert line endings to lf
  test           Run tests with coverage
  sec            Security check with bandit
  all

Use poe all to run all tasks before committing any changes.

Maintainers

@titusz

Contributing

Pull requests are welcome. For significant changes, please open an issue first to discuss your plans. Please make sure to update tests as appropriate.

You may also want join our developer chat on Telegram at https://t.me/iscc_dev.