Performing Steganography Detection

# Install steganography detection tools
sudo apt-get install steghide stegsnow
pip install zsteg
pip install stegoveritas
gem install zsteg  # Ruby-based tool for PNG/BMP

# Examine file metadata for anomalies
exiftool /cases/case-2024-001/media/suspect_image.jpg | tee /cases/case-2024-001/analysis/metadata.txt

# Check for unusual file size (larger than expected for resolution/format)
identify -verbose /cases/case-2024-001/media/suspect_image.jpg | head -30

# Verify file type matches extension
file /cases/case-2024-001/media/suspect_image.jpg
# Confirm JPEG signature vs actual content

# Check for appended data after file footer
python3 << 'PYEOF'
import os

filepath = '/cases/case-2024-001/media/suspect_image.jpg'
filesize = os.path.getsize(filepath)

with open(filepath, 'rb') as f:
    data = f.read()

# JPEG files end with FF D9
jpeg_end = data.rfind(b'\xff\xd9')
if jpeg_end > 0:
    trailing_bytes = filesize - jpeg_end - 2
    if trailing_bytes > 0:
        print(f"WARNING: {trailing_bytes} bytes of data after JPEG end marker!")
        print(f"  File size: {filesize} bytes")
        print(f"  JPEG data: {jpeg_end + 2} bytes")
        print(f"  Hidden data: {trailing_bytes} bytes")
        # Extract trailing data
        with open('/cases/case-2024-001/analysis/trailing_data.bin', 'wb') as out:
            out.write(data[jpeg_end + 2:])
    else:
        print("No trailing data detected after JPEG end marker")

# Check for embedded ZIP/RAR archives
zip_offset = data.find(b'PK\x03\x04')
rar_offset = data.find(b'Rar!\x1a\x07')
if zip_offset > 0:
    print(f"ZIP archive found at offset {zip_offset}")
if rar_offset > 0:
    print(f"RAR archive found at offset {rar_offset}")
PYEOF

# Use binwalk to detect embedded files and data
binwalk /cases/case-2024-001/media/suspect_image.jpg | tee /cases/case-2024-001/analysis/binwalk_scan.txt

# Extract embedded files
binwalk --extract --directory /cases/case-2024-001/analysis/binwalk_extracted/ \
   /cases/case-2024-001/media/suspect_image.jpg

# Use zsteg for PNG and BMP analysis (LSB detection)
zsteg /cases/case-2024-001/media/suspect_image.png | tee /cases/case-2024-001/analysis/zsteg_results.txt

# zsteg with all checks
zsteg -a /cases/case-2024-001/media/suspect_image.png

# Use stegoveritas for comprehensive analysis
stegoveritas /cases/case-2024-001/media/suspect_image.jpg \
   -out /cases/case-2024-001/analysis/stegoveritas/

# Stegoveritas performs:
# - Metadata extraction
# - LSB analysis (multiple bit planes)
# - Color map analysis
# - Trailing data detection
# - Embedded file extraction
# - Image transformation analysis

# Use steghide for JPEG/BMP/WAV/AU extraction attempts
# Try with empty password
steghide extract -sf /cases/case-2024-001/media/suspect_image.jpg -p "" \
   -xf /cases/case-2024-001/analysis/steghide_extract.bin 2>&1

# Try with common passwords
for pwd in password secret hidden stego test 123456 admin; do
    result=$(steghide extract -sf /cases/case-2024-001/media/suspect_image.jpg \
       -p "$pwd" -xf "/cases/case-2024-001/analysis/steghide_$pwd.bin" 2>&1)
    if echo "$result" | grep -q "extracted"; then
        echo "SUCCESS with password: $pwd"
    fi
done

# Custom LSB analysis with Python
python3 << 'PYEOF'
from PIL import Image
import numpy as np

img = Image.open('/cases/case-2024-001/media/suspect_image.png')
pixels = np.array(img)

# Extract LSB from each color channel
for channel, name in enumerate(['Red', 'Green', 'Blue']):
    if channel >= pixels.shape[2]:
        break

    lsb_data = pixels[:, :, channel] & 1

    # Count distribution (should be ~50/50 for natural images)
    zeros = np.sum(lsb_data == 0)
    ones = np.sum(lsb_data == 1)
    total = zeros + ones
    ratio = ones / total

    print(f"{name} channel LSB: 0s={zeros} ({zeros/total*100:.1f}%), 1s={ones} ({ones/total*100:.1f}%)")
    if abs(ratio - 0.5) < 0.01:
        print(f"  NEUTRAL - Close to random (could be stego or natural)")
    elif ratio > 0.55 or ratio < 0.45:
        print(f"  ANOMALY - Significant deviation from expected distribution")

# Extract LSB data as bytes
lsb_bits = (pixels[:, :, 0] & 1).flatten()
lsb_bytes = np.packbits(lsb_bits)

# Check if extracted data has structure
with open('/cases/case-2024-001/analysis/lsb_extracted.bin', 'wb') as f:
    f.write(lsb_bytes.tobytes())

# Check for known file signatures in extracted data
import struct
header = bytes(lsb_bytes[:16])
print(f"\nLSB extracted header (hex): {header.hex()}")
if header[:4] == b'PK\x03\x04':
    print("  DETECTED: ZIP archive in LSB data!")
elif header[:3] == b'GIF':
    print("  DETECTED: GIF image in LSB data!")
elif header[:4] == b'\x89PNG':
    print("  DETECTED: PNG image in LSB data!")
elif header[:2] == b'\xff\xd8':
    print("  DETECTED: JPEG image in LSB data!")

# Generate LSB visualization
lsb_img = Image.fromarray((lsb_data * 255).astype(np.uint8))
lsb_img.save('/cases/case-2024-001/analysis/lsb_visualization.png')
print("\nLSB visualization saved to lsb_visualization.png")
PYEOF

# Spectral analysis of audio files
python3 << 'PYEOF'
import wave
import numpy as np

# Analyze WAV file for audio steganography
with wave.open('/cases/case-2024-001/media/suspect_audio.wav', 'r') as wav:
    frames = wav.readframes(wav.getnframes())
    samples = np.frombuffer(frames, dtype=np.int16)

    # LSB analysis of audio samples
    lsb = samples & 1
    zeros = np.sum(lsb == 0)
    ones = np.sum(lsb == 1)
    total = len(lsb)

    print(f"Audio LSB Analysis:")
    print(f"  Samples: {total}")
    print(f"  LSB 0s: {zeros} ({zeros/total*100:.1f}%)")
    print(f"  LSB 1s: {ones} ({ones/total*100:.1f}%)")

    # Extract LSB data
    lsb_bytes = np.packbits(lsb)
    with open('/cases/case-2024-001/analysis/audio_lsb.bin', 'wb') as f:
        f.write(lsb_bytes.tobytes())

    # Chi-square test for randomness
    from scipy import stats
    chi2, p_value = stats.chisquare([zeros, ones])
    print(f"  Chi-square: {chi2:.4f}, p-value: {p_value:.4f}")
    if p_value < 0.05:
        print(f"  ANOMALY: LSB distribution is not random (potential stego)")
PYEOF

# Use steghide on audio files
steghide info /cases/case-2024-001/media/suspect_audio.wav

# Analyze with sonic-visualiser or audacity for spectral anomalies
# (Check spectrogram for hidden images encoded in frequency domain)

# Compile findings
python3 << 'PYEOF'
import os, json

report = {
    "case": "2024-001",
    "files_analyzed": [],
    "findings": []
}

analysis_dir = '/cases/case-2024-001/analysis/'
for f in os.listdir(analysis_dir):
    if f.endswith('.txt'):
        with open(os.path.join(analysis_dir, f)) as fh:
            content = fh.read()
            if 'DETECTED' in content or 'SUCCESS' in content or 'WARNING' in content:
                report["findings"].append({
                    "source": f,
                    "content": content[:500]
                })

with open('/cases/case-2024-001/analysis/steg_report.json', 'w') as f:
    json.dump(report, f, indent=2)

print("Steganalysis report generated")
print(f"Total findings: {len(report['findings'])}")
PYEOF