How Accurate Is Magic Number Detection for Identifying File Types?

Understanding Magic Number Detection Accuracy

Magic number detection achieves remarkably high accuracy for binary file formats with well-defined headers, typically reaching near 100% reliability for common formats like images, executables, archives, and media files. However, accuracy varies significantly depending on file type, database quality, and implementation approach.

The question isn't simply whether magic number detection is accurate - it's understanding when it's highly reliable, when it has limitations, and how to account for those limitations in security and validation systems.

Accuracy Breakdown by File Category

Binary Files with Well-Defined Headers: 95-100% Accuracy

File formats with mandatory, unique magic numbers achieve the highest detection accuracy:

Image Formats:

• PNG: ~100% accuracy (unique signature 89 50 4E 47)
• GIF: ~100% accuracy (distinctive GIF87a or GIF89a)
• BMP: ~100% accuracy (starts with 42 4D - "BM")
• JPEG: 95-98% accuracy (multiple valid signatures, see below)

Executable Formats:

• Windows PE: ~100% accuracy (4D 5A - "MZ")
• Linux ELF: ~100% accuracy (7F 45 4C 46)
• Mach-O: ~100% accuracy (FE ED FA CE or variations)

Archive Formats:

• ZIP: ~100% accuracy (50 4B 03 04 - "PK")
• RAR: ~100% accuracy (52 61 72 21 - "Rar!")
• 7-Zip: ~100% accuracy (37 7A BC AF 27 1C)
• GZIP: ~100% accuracy (1F 8B)

Document Formats:

• PDF: ~100% accuracy (25 50 44 46 - "%PDF")
• Office Open XML: 90-95% accuracy (ZIP-based, requires additional analysis)

These formats mandate specific headers for parsers to process files correctly, making their magic numbers extremely reliable identifiers.

Formats with Multiple Valid Magic Numbers: 90-95% Accuracy

Some file formats accept multiple valid magic number sequences, requiring comprehensive signature databases:

JPEG Images:

JPEG files can begin with several different byte sequences, all indicating legitimate JPEG format:

• FF D8 FF E0 - JFIF (JPEG File Interchange Format)
• FF D8 FF DB - Raw JPEG
• FF D8 FF EE - JPEG with Adobe metadata
• FF D8 FF E1 - JPEG with EXIF data (digital camera photos)

A magic number detector must recognize all these variants to achieve high JPEG detection accuracy. Missing variants in the signature database leads to false negatives where legitimate JPEG files aren't identified.

MP3 Audio:

MP3 files may start with:

• 49 44 33 - ID3v2 tag
• FF FB or FF F3 - MPEG audio frame sync

Detection systems must account for both ID3-tagged and raw MPEG audio streams.

TIFF Images:

TIFF files have two valid byte orders:

• 49 49 2A 00 - Little-endian ("II")
• 4D 4D 00 2A - Big-endian ("MM")

Both are valid TIFF signatures depending on the system that created the file.

Formats with Shared Magic Numbers: 70-85% Accuracy

Multiple file types sharing identical or similar magic numbers require additional analysis beyond header examination:

Microsoft Office Formats (.docx, .xlsx, .pptx):

All modern Microsoft Office files use the ZIP-based Office Open XML format, sharing the magic number 50 4B 03 04. Distinguishing between:

• Word documents (.docx)
• Excel spreadsheets (.xlsx)
• PowerPoint presentations (.pptx)

Requires examining internal ZIP structure and content types, not just the magic number. Simple magic number detection returns "ZIP archive" without format-specific identification.

Container Formats:

Many modern file formats use container structures (ZIP, OGG, MP4) as their foundation:

• EPUB ebooks: ZIP container with specific internal structure
• JAR files: ZIP container with Java manifest
• Android APK: ZIP container with Android-specific contents
• OGG media: OGG container with various codec options

Magic number detection identifies the container but not the specific format without deeper inspection.

Plain Text Formats: 0% Accuracy

As discussed in our detailed article on plain text files, text-based formats cannot be identified through magic numbers:

• CSV: No magic number, just plain text
• TXT: No magic number, any text content
• LOG: No magic number, format-agnostic text
• Markdown: No magic number, plain text with conventions
• Source code: No magic numbers for .py, .js, .java, etc.

Magic number detection achieves 0% accuracy for these formats because they lack binary signatures entirely.

Factors Affecting Detection Accuracy

1. Database Quality and Completeness

Detection accuracy depends heavily on the comprehensiveness of the magic number signature database:

Commercial Databases:

• libmagic (used by Unix file command): 5,000+ signatures, regularly updated
• TrID (TrIDNet): 10,000+ file type definitions with pattern matching
• Apache Tika: Enterprise-grade detection with 1,000+ supported formats

Free/Limited Databases:

• May contain only common formats (100-500 signatures)
• Infrequent updates miss new format variants
• May lack region-specific or specialized formats

Higher-quality databases improve accuracy by recognizing more format variants and edge cases.

2. Signature Ambiguity

Some magic number sequences are substrings of others, creating potential misidentification:

Example: ISO and UDF

• ISO 9660 CD image: 43 44 30 30 31 at offset 32769
• UDF filesystem: May share similar patterns

Detectors must check multiple offsets and validate full signature patterns, not just initial bytes.

3. File Corruption or Malformation

Corrupted files with damaged headers reduce detection accuracy:

Normal PNG: 89 50 4E 47 0D 0A 1A 0A
Corrupted:   89 50 4E 00 0D 0A 1A 0A
             ↑ Corruption here

Strict detectors reject corrupted files as "unknown," while lenient detectors may attempt fuzzy matching with reduced confidence.

4. Encryption and Compression

Encrypted files present magic numbers of the encryption container, not the original format:

• PGP encrypted: Shows PGP signature, not underlying file type
• Encrypted ZIP: Shows encryption header, not ZIP signature
• Password-protected PDF: Still shows PDF signature (encryption is internal)

Compressed files show compression format rather than original content format.

5. Offset Variations

While most magic numbers appear at byte 0, some formats have signatures at specific offsets:

Format	Offset	Signature
ISO 9660	32769	CD001
ext2/ext3	1080	53 EF
TAR (ustar)	257	ustar

Detectors checking only offset 0 miss these formats, reducing overall accuracy.

Implementation Quality Affects Accuracy

Comprehensive Detection Approach

High-accuracy implementations combine multiple techniques:

def detect_file_type_comprehensive(file_path):
    """Multi-layer file type detection"""

    # Layer 1: Magic number detection at offset 0
    with open(file_path, 'rb') as f:
        header = f.read(8)
        primary_type = check_magic_numbers(header)

        # Layer 2: Check additional offsets if no match
        if not primary_type:
            f.seek(32769)  # ISO offset
            iso_sig = f.read(5)
            if iso_sig == b'CD001':
                return 'iso'

            f.seek(257)  # TAR offset
            tar_sig = f.read(5)
            if tar_sig == b'ustar':
                return 'tar'

        # Layer 3: For containers, analyze internal structure
        if primary_type == 'zip':
            return analyze_zip_contents(file_path)

        # Layer 4: Validate file structure beyond header
        if primary_type:
            if validate_file_structure(file_path, primary_type):
                return primary_type

    # Layer 5: Heuristic analysis for text files
    return heuristic_text_analysis(file_path)

AI-Powered Detection (2025)

Modern detection tools employ machine learning for improved accuracy:

Google's Magika:

• Uses deep learning to understand file structure and content
• Achieves >99% precision on ~100 content types
• Significantly outperforms traditional magic number detection
• Resistant to spoofing through structure understanding

Traditional magic number detection accuracy is inherently limited compared to AI approaches that analyze complete file structure, not just headers.

When to Trust Magic Number Detection

High Confidence Scenarios

Magic number detection is highly reliable when:

1. Common binary formats: PNG, JPEG, GIF, PDF, ZIP, EXE
2. Well-defined specifications: Formats with mandatory headers
3. Updated signature databases: Recent libmagic or commercial tools
4. No shared signatures: Unique magic numbers without ambiguity
5. Unencrypted files: Clear access to format headers

Medium Confidence Scenarios

Exercise caution with:

1. Container formats: ZIP, OGG, MP4 - need deeper analysis
2. Multiple variants: JPEG, TIFF - ensure all variants recognized
3. Offset-based signatures: ISO, TAR - check multiple offsets
4. Compressed files: May show compression format, not content
5. Old or obscure formats: May not be in signature databases

Low Confidence Scenarios

Don't rely on magic number detection for:

1. Plain text files: CSV, TXT, LOG - no magic numbers
2. Source code: .py, .js, .java - text-based
3. Encrypted files: Shows encryption container, not content
4. Heavily corrupted files: Damaged headers prevent identification
5. Intentionally obfuscated files: Spoofing attempts

Improving Detection Accuracy

For Developers

1. Use comprehensive libraries: Prefer libmagic, Apache Tika, or TrID over simple implementations
2. Check multiple offsets: Don't assume all signatures at byte 0
3. Validate file structure: Confirm entire file matches claimed format
4. Handle container formats: Analyze internal structure for specific identification
5. Keep databases updated: Regular signature database updates
6. Implement confidence scoring: Return probability scores, not binary yes/no

For Security Professionals

1. Layer detection methods: Combine magic numbers with extension, MIME type, and content analysis
2. Understand limitations: Know which formats cannot be identified
3. Use AI-enhanced tools: Consider ML-based detection for critical applications
4. Validate with parsers: Attempt to parse files with format-specific libraries
5. Monitor false positives/negatives: Track detection accuracy in your environment

Best Practices

def robust_file_identification(file_path):
    """Robust multi-method file identification"""

    results = {
        'magic_number': None,
        'extension': None,
        'mime_type': None,
        'parser_validation': None,
        'confidence': 0.0
    }

    # Method 1: Magic number detection
    import magic
    m = magic.Magic(mime=True)
    results['magic_number'] = m.from_file(file_path)

    # Method 2: Extension analysis
    _, ext = os.path.splitext(file_path)
    results['extension'] = ext.lower()

    # Method 3: Format-specific parser validation
    try:
        if results['magic_number'] == 'image/jpeg':
            from PIL import Image
            img = Image.open(file_path)
            img.verify()
            results['parser_validation'] = True
            results['confidence'] = 0.95
    except:
        results['parser_validation'] = False
        results['confidence'] = 0.60

    # Consensus decision
    if (results['magic_number'] and
        results['parser_validation'] and
        extension_matches_mime(results['extension'], results['magic_number'])):
        results['confidence'] = 0.98

    return results

Real-World Accuracy Statistics

Based on security research and tool evaluations:

File Command (libmagic) Accuracy:

• Common binary formats: 98-100%
• Container formats requiring deep inspection: 75-85%
• Obscure or region-specific formats: 60-70%
• Plain text formats: 0%
• Overall corpus accuracy: ~85-90%

Apache Tika Accuracy:

• Enterprise documents: 95-98%
• Media files: 90-95%
• Archives: 95-98%
• Overall: 90-95%

Google Magika (AI-based):

• Precision: >99% on benchmark dataset
• Coverage: ~100 common content types
• Resistance to spoofing: Significantly higher than traditional methods

Conclusion

Magic number detection achieves highly accurate file type identification for binary formats with well-defined headers, typically reaching 95-100% accuracy for common formats like PNG, PDF, ZIP, and executable files. However, accuracy varies considerably based on file type, database quality, and implementation approach.

Factors limiting accuracy include:

• Plain text formats lacking magic numbers (0% accuracy)
• Formats with multiple valid magic numbers requiring comprehensive databases
• Shared magic numbers in container formats needing deeper analysis
• File corruption, encryption, or intentional obfuscation

For production systems, combine magic number detection with extension validation, MIME type checking, parser verification, and content analysis to achieve the highest reliability. Modern AI-powered tools like Google's Magika significantly improve accuracy through deep learning-based structure understanding.

Understanding these accuracy characteristics helps security professionals and developers make informed decisions about when to trust magic number detection and when to employ additional validation layers.

Our File Magic Number Checker tool uses comprehensive signature databases to identify file types, but remember to combine it with other validation methods for security-critical applications. All file analysis happens entirely in your browser for maximum privacy.