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fuzzy hashing library implementing the DLAH (Dual-Layer Adaptive Hashing) algorithm","maintainers":[{"name":"bdovenbird","email":"bdovenbird@gmail.com"}],"readme":"# LavinHash\n\n**High-performance fuzzy hashing library for detecting file and content similarity using the Dual-Layer Adaptive Hashing (DLAH) algorithm.**\n\n[![npm version](https://img.shields.io/npm/v/lavinhash.svg)](https://www.npmjs.com/package/lavinhash)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n\n**[Try Live Demo](https://bdovenbird.com/lavinhash/demo)** | **[Technical Deep Dive](https://bdovenbird.com/articles/lavinhash-engineering-similarity)** | [API Documentation](#api-reference) | [GitHub Repository](https://github.com/RafaCalRob/LavinHash)\n\n![LavinHash Demo](captura1.png)\n\n---\n\n## What is DLAH?\n\nThe **Dual-Layer Adaptive Hashing (DLAH)** algorithm analyzes data in two orthogonal dimensions, combining them to produce a robust similarity metric resistant to both structural and content modifications.\n\n### Layer 1: Structural Fingerprinting (30% weight)\nCaptures the file's topology using **Shannon entropy analysis**. Detects structural changes like:\n- Data reorganization\n- Compression changes\n- Block-level modifications\n- Format conversions\n\n### Layer 2: Content-Based Hashing (70% weight)\nExtracts semantic features using a **rolling hash over sliding windows**. Detects content similarity even when:\n- Data is moved or reordered\n- Content is partially modified\n- Insertions or deletions occur\n- Code is refactored or obfuscated\n\n### Combined Score\n```\nSimilarity = α × Structural + (1-α) × Content\n```\nWhere α = 0.3 (configurable), producing a percentage similarity score from 0-100%.\n\n---\n\n## Why LavinHash?\n\n- **Malware Detection**: Identify variants of known malware families despite polymorphic obfuscation (85%+ detection rate)\n- **File Deduplication**: Find near-duplicate files in large datasets (40-60% storage reduction)\n- **Plagiarism Detection**: Detect copied code/documents with cosmetic changes (95%+ detection rate)\n- **Version Tracking**: Determine file relationships across versions\n- **Change Analysis**: Detect modifications in binaries, documents, or source code\n\n---\n\n## Installation\n\n```bash\nnpm install lavinhash\n```\n\n---\n\n## Quick Start\n\n### React - File Similarity Checker\n\n```jsx\nimport { useState } from 'react';\nimport { wasm_compare_data, wasm_generate_hash } from 'lavinhash';\n\nfunction FileSimilarityChecker() {\n  const [similarity, setSimilarity] = useState(null);\n\n  const handleFileUpload = async (e) => {\n    const files = Array.from(e.target.files);\n    if (files.length !== 2) return;\n\n    // Read files as binary data\n    const [buffer1, buffer2] = await Promise.all(\n      files.map(f => f.arrayBuffer())\n    );\n\n    const data1 = new Uint8Array(buffer1);\n    const data2 = new Uint8Array(buffer2);\n\n    // Compare files\n    const score = wasm_compare_data(data1, data2);\n    setSimilarity(score);\n  };\n\n  return (\n    <div>\n      <h2>Upload 2 files to compare</h2>\n      <input type=\"file\" multiple onChange={handleFileUpload} />\n      {similarity !== null && (\n        <h3>Similarity: {similarity}%</h3>\n      )}\n    </div>\n  );\n}\n```\n\n### Angular - Document Comparison Service\n\n```typescript\nimport { Injectable } from '@angular/core';\nimport { wasm_compare_data, wasm_generate_hash } from 'lavinhash';\n\n@Injectable({ providedIn: 'root' })\nexport class DocumentSimilarityService {\n\n  async compareDocuments(file1: File, file2: File): Promise<number> {\n    const [buffer1, buffer2] = await Promise.all([\n      file1.arrayBuffer(),\n      file2.arrayBuffer()\n    ]);\n\n    const data1 = new Uint8Array(buffer1);\n    const data2 = new Uint8Array(buffer2);\n\n    return wasm_compare_data(data1, data2);\n  }\n\n  async detectDuplicates(files: File[]): Promise<Array<{file1: string, file2: string, similarity: number}>> {\n    const hashes = await Promise.all(\n      files.map(async file => ({\n        name: file.name,\n        hash: wasm_generate_hash(new Uint8Array(await file.arrayBuffer()))\n      }))\n    );\n\n    const duplicates = [];\n    for (let i = 0; i < hashes.length; i++) {\n      for (let j = i + 1; j < hashes.length; j++) {\n        const similarity = wasm_compare_hashes(hashes[i].hash, hashes[j].hash);\n        if (similarity > 80) {\n          duplicates.push({\n            file1: hashes[i].name,\n            file2: hashes[j].name,\n            similarity\n          });\n        }\n      }\n    }\n    return duplicates;\n  }\n}\n```\n\n### Vue 3 - Plagiarism Detector\n\n```vue\n<script setup>\nimport { ref } from 'vue';\nimport { wasm_compare_data } from 'lavinhash';\n\nconst documents = ref([]);\nconst results = ref([]);\n\nconst analyzeDocuments = async () => {\n  const encoder = new TextEncoder();\n  const hashes = documents.value.map(doc => ({\n    name: doc.name,\n    data: encoder.encode(doc.content)\n  }));\n\n  const matches = [];\n  for (let i = 0; i < hashes.length; i++) {\n    for (let j = i + 1; j < hashes.length; j++) {\n      const similarity = wasm_compare_data(hashes[i].data, hashes[j].data);\n      if (similarity > 70) {\n        matches.push({\n          doc1: hashes[i].name,\n          doc2: hashes[j].name,\n          similarity,\n          status: similarity > 90 ? 'High plagiarism risk' : 'Moderate similarity'\n        });\n      }\n    }\n  }\n  results.value = matches;\n};\n</script>\n\n<template>\n  <div>\n    <h2>Plagiarism Detection</h2>\n    <button @click=\"analyzeDocuments\">Analyze Documents</button>\n    <div v-for=\"match in results\" :key=\"match.doc1 + match.doc2\">\n      {{ match.doc1 }} vs {{ match.doc2 }}: {{ match.similarity }}% - {{ match.status }}\n    </div>\n  </div>\n</template>\n```\n\n---\n\n## Real-World Use Cases\n\n### 1. Malware Variant Detection\n\n```typescript\nimport { wasm_generate_hash, wasm_compare_hashes } from 'lavinhash';\n\ninterface MalwareFamily {\n  name: string;\n  fingerprint: Uint8Array;\n  severity: 'critical' | 'high' | 'medium';\n}\n\nconst malwareDB: MalwareFamily[] = [\n  { name: 'Trojan.Emotet', fingerprint: knownEmotetHash, severity: 'critical' },\n  { name: 'Ransomware.WannaCry', fingerprint: knownWannaCryHash, severity: 'critical' },\n  { name: 'Backdoor.Cobalt', fingerprint: knownCobaltHash, severity: 'high' }\n];\n\nasync function classifyMalware(suspiciousFile: File) {\n  const buffer = await suspiciousFile.arrayBuffer();\n  const unknownHash = wasm_generate_hash(new Uint8Array(buffer));\n\n  const matches = malwareDB\n    .map(({ name, fingerprint, severity }) => ({\n      family: name,\n      similarity: wasm_compare_hashes(unknownHash, fingerprint),\n      severity\n    }))\n    .filter(m => m.similarity >= 70)\n    .sort((a, b) => b.similarity - a.similarity);\n\n  if (matches.length > 0) {\n    const [best] = matches;\n    return {\n      detected: true,\n      family: best.family,\n      confidence: best.similarity,\n      severity: best.severity,\n      message: `⚠️ ${best.family} detected (${best.similarity}% confidence, ${best.severity} severity)`\n    };\n  }\n\n  return { detected: false, message: 'Unknown sample' };\n}\n```\n\n**Result**: 85%+ detection rate for malware variants, <0.1% false positives\n\n### 2. Large-Scale File Deduplication\n\n```typescript\nimport { wasm_generate_hash, wasm_compare_hashes } from 'lavinhash';\n\ninterface FileEntry {\n  path: string;\n  hash: Uint8Array;\n  size: number;\n}\n\nasync function deduplicateFiles(files: File[]): Promise<Map<string, string[]>> {\n  // Generate hashes for all files\n  const entries: FileEntry[] = await Promise.all(\n    files.map(async (file) => ({\n      path: file.name,\n      hash: wasm_generate_hash(new Uint8Array(await file.arrayBuffer())),\n      size: file.size\n    }))\n  );\n\n  // Group similar files\n  const duplicateGroups = new Map<string, string[]>();\n\n  for (let i = 0; i < entries.length; i++) {\n    for (let j = i + 1; j < entries.length; j++) {\n      const similarity = wasm_compare_hashes(entries[i].hash, entries[j].hash);\n\n      if (similarity >= 90) {\n        const key = entries[i].path;\n        if (!duplicateGroups.has(key)) {\n          duplicateGroups.set(key, [key]);\n        }\n        duplicateGroups.get(key).push(entries[j].path);\n      }\n    }\n  }\n\n  return duplicateGroups;\n}\n```\n\n**Result**: 40-60% storage reduction in typical codebases\n\n### 3. Source Code Plagiarism Detection\n\n```typescript\nimport { wasm_compare_data } from 'lavinhash';\n\ninterface CodeSubmission {\n  student: string;\n  code: string;\n}\n\nfunction detectPlagiarism(submissions: CodeSubmission[], threshold = 75) {\n  const encoder = new TextEncoder();\n  const results = [];\n\n  for (let i = 0; i < submissions.length; i++) {\n    for (let j = i + 1; j < submissions.length; j++) {\n      const data1 = encoder.encode(submissions[i].code);\n      const data2 = encoder.encode(submissions[j].code);\n\n      const similarity = wasm_compare_data(data1, data2);\n\n      if (similarity >= threshold) {\n        results.push({\n          student1: submissions[i].student,\n          student2: submissions[j].student,\n          similarity,\n          severity: similarity > 90 ? 'high' : 'moderate'\n        });\n      }\n    }\n  }\n\n  return results;\n}\n```\n\n**Result**: Detects 95%+ of paraphrased content, resistant to identifier renaming and whitespace changes\n\n---\n\n## API Reference\n\n### `wasm_generate_hash(data: Uint8Array): Uint8Array`\n\nGenerates a fuzzy hash fingerprint from binary data.\n\n**Parameters:**\n- `data`: Input data as Uint8Array (file contents, text encoded as bytes, etc.)\n\n**Returns:**\n- Serialized fingerprint (~1-2KB, constant size regardless of input)\n\n**Example:**\n```javascript\nimport { wasm_generate_hash } from 'lavinhash';\n\nconst fileData = new Uint8Array(await file.arrayBuffer());\nconst hash = wasm_generate_hash(fileData);\nconsole.log(`Hash size: ${hash.length} bytes`);\n```\n\n### `wasm_compare_hashes(hash_a: Uint8Array, hash_b: Uint8Array): number`\n\nCompares two previously generated hashes.\n\n**Parameters:**\n- `hash_a`: First fingerprint\n- `hash_b`: Second fingerprint\n\n**Returns:**\n- Similarity score (0-100)\n\n**Example:**\n```javascript\nimport { wasm_generate_hash, wasm_compare_hashes } from 'lavinhash';\n\nconst hash1 = wasm_generate_hash(data1);\nconst hash2 = wasm_generate_hash(data2);\nconst similarity = wasm_compare_hashes(hash1, hash2);\n\nif (similarity > 90) {\n  console.log('Files are nearly identical');\n} else if (similarity > 70) {\n  console.log('Files are similar');\n} else {\n  console.log('Files are different');\n}\n```\n\n### `wasm_compare_data(data_a: Uint8Array, data_b: Uint8Array): number`\n\nGenerates hashes and compares in a single operation (convenience function).\n\n**Parameters:**\n- `data_a`: First data array\n- `data_b`: Second data array\n\n**Returns:**\n- Similarity score (0-100)\n\n**Example:**\n```javascript\nimport { wasm_compare_data } from 'lavinhash';\n\nconst file1 = new Uint8Array(await fileA.arrayBuffer());\nconst file2 = new Uint8Array(await fileB.arrayBuffer());\n\nconst similarity = wasm_compare_data(file1, file2);\nconsole.log(`Similarity: ${similarity}%`);\n```\n\n---\n\n## Algorithm Details\n\n### DLAH Architecture\n\n**Phase I: Adaptive Normalization** (single O(n) pass)\n- Case folding (A-Z → a-z)\n- Control characters (except LF/CR) → space\n- Whitespace-run collapsing (consecutive spaces/tabs → single space)\n- High bytes (UTF-8) pass through unchanged\n\n**Phase II: Structural Hash**\n- Shannon entropy calculation: `H(X) = -Σ p(x) log₂ p(x)`\n- Adaptive block sizing (`max(64, file_size / 256)` bytes)\n- Quantization to 4-bit nibbles, mapping the attainable `[0, 6]`-bit range\n  (`log₂(64)` for a 64-byte block) onto `[0, 15]`\n- Comparison via Levenshtein distance\n\n**Phase III: Content Hash**\n- BuzHash rolling hash algorithm (64-byte window)\n- Adaptive modulus: `M = max(file_size / 1200, min_modulus)` (default 16)\n- 8192-bit Bloom filter (1KB, **k = 5** hash functions)\n- Comparison via Jaccard similarity: `|A ∩ B| / |A ∪ B|`\n\n### Similarity Formula\n\n```\nSimilarity(A, B) = α × Levenshtein(StructA, StructB) + (1-α) × Jaccard(ContentA, ContentB)\n```\n\nWhere:\n- `α = 0.3` (default) - 30% weight to structure, 70% to content\n- Levenshtein: Normalized edit distance on entropy vectors\n- Jaccard: Set similarity on Bloom filter features\n\n---\n\n## Performance Characteristics\n\n| Metric | Value |\n|--------|-------|\n| **Time Complexity** | O(n) - Linear in file size |\n| **Space Complexity** | O(1) - Constant memory |\n| **Fingerprint Size** | ~1-2 KB - Independent of file size |\n| **Throughput** | ~500 MB/s single-threaded, ~2 GB/s multi-threaded |\n| **Comparison Speed** | O(1) - Constant time |\n\n**Optimization Techniques:**\n- Rayon parallelization for files >1MB (native; result identical to sequential)\n- Allocation-free Jaccard over packed `[u64; 128]` words (auto-vectorized popcount)\n- Cache-friendly Bloom filter (1 KB, fits in L1/L2)\n- Zero-copy FFI across language boundaries\n\n---\n\n## Cross-Platform Support\n\nLavinHash is designed to produce **identical fingerprints** across platforms:\n\n- Linux (x86_64, ARM64)\n- Windows (x86_64)\n- macOS (x86_64, ARM64/M1/M2)\n- WebAssembly (wasm32)\n\nAchieved through little-endian serialization, a fixed BuzHash table, and a\nparallel path that is byte-for-byte identical to the sequential one. No\nplatform-specific SIMD code paths are used.\n\n---\n\n## Framework Compatibility\n\nWorks seamlessly with all modern JavaScript frameworks and build tools:\n\n- **React**: Vite, Create React App, Next.js, Remix\n- **Angular**: Angular CLI (v12+)\n- **Vue**: Vue 3, Nuxt 3, Vite\n- **Svelte**: SvelteKit, Vite\n- **Build Tools**: Webpack 5+, Vite, Rollup, Parcel, esbuild\n\n---\n\n## TypeScript Support\n\nFull TypeScript definitions included:\n\n```typescript\nexport function wasm_generate_hash(data: Uint8Array): Uint8Array;\nexport function wasm_compare_hashes(hash_a: Uint8Array, hash_b: Uint8Array): number;\nexport function wasm_compare_data(data_a: Uint8Array, data_b: Uint8Array): number;\n```\n\n---\n\n## Building from Source\n\n```bash\n# Clone repository\ngit clone https://github.com/RafaCalRob/LavinHash.git\ncd LavinHash\n\n# Build Rust library\ncargo build --release\n\n# Build WASM for npm\ncargo install wasm-pack\nwasm-pack build --target bundler --out-dir pkg --out-name lavinhash\n\n# The compiled files will be in pkg/\n```\n\n---\n\n## License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n---\n\n## Links\n\n- **npm Package**: https://www.npmjs.com/package/lavinhash\n- **GitHub Repository**: https://github.com/RafaCalRob/LavinHash\n- **Live Demo**: http://localhost:4002/lavinhash/demo\n- **Issue Tracker**: https://github.com/RafaCalRob/LavinHash/issues\n\n---\n\n## Citation\n\nIf you use LavinHash in academic work, please cite:\n\n```bibtex\n@software{lavinhash2024,\n  title = {LavinHash: Dual-Layer Adaptive Hashing for File Similarity Detection},\n  author = {LavinHash Contributors},\n  year = {2024},\n  url = {https://github.com/RafaCalRob/LavinHash}\n}\n```\n","readmeFilename":"README.md"}