Invisible Geolocation Signature In A Single Image

• doi: 10.1109/ICASSP.2018.8461717
• title: Invisible Geo-Location Signature in A Single Image
• publisher: IEEE
• isbn: 978-1-5386-4659-5
• issn: 2379-190X
• rank: 2833
• access_type: LOCKED
• content_type: Conferences
• abstract: Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information. It is, however, very challenging to extract an ENF signal from a single image, as compared to the recent art in extracting ENF traces from audio and video. This paper presents novel investigations toward this challenge, by synergistically exploring the rolling shutter effect of CMOS imaging sensors and entropy differences of composite signals. We study quantitatively the relationship between the ENF strength and its detectability from a single image, and bring out a unique forensics capability of invisible traces that shine a light on an image's capturing location.
• article_number: 8461717
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8461717
• html_url: https://ieeexplore.ieee.org/document/8461717/
• abstract_url: https://ieeexplore.ieee.org/document/8461717/
• publication_title: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• conference_location: Calgary, AB, Canada
• conference_dates: 15-20 April 2018
• publication_number: 8450881
• is_number: 8461260
• publication_year: 2018
• publication_date: 15-20 April 2018
• start_page: 1987
• end_page: 1991
• citing_paper_count: 18
• citing_patent_count: 0
• download_count: 376
• insert_date: 20180913
• index_terms:
  • ieee_terms:
    • Entropy
    • Cameras
    • Frequency estimation
    • Time-frequency analysis
    • Cost function
    • Estimation
    • Histograms
  • author_terms:
    • Geo- Tagging
    • Frequency Estimation
    • Electric Network Frequency (ENF)
    • Rolling Shutter
  • dynamic_index_terms:
    • Single Image
    • National Security
    • Security Sector
    • National Defense
    • National Defence
    • Forensic
    • Journalism
    • Power Grid
    • Image Sensor
    • Camera Sensor
    • Unique Capabilities
    • Rolling Shutter
    • Illumination
    • Lighting
    • Corruption
    • Cost Function
    • Audio Recordings
    • Sound Recordings
    • Natural Images
    • Nominal Value
    • Frequency Estimation
    • Sinusoidal Signal
    • Visual Content
    • Challenging Scenarios
    • Images In Row
    • Real-world Images
    • Exposure Bias
    • Biased Exposure
    • Readout Time
    • Complementary Metal-oxide Semiconductor Camera
    • Complementary Metal-oxide-semiconductor Camera
    • Minimum Entropy
    • Power Distribution Network
    • Increase In Entropy
    • Images In Column
    • Global Bias
    • Global Biases
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-5386-4659-5, isbnType: New-2005
  • format: USB ISBN, value: 978-1-5386-4657-1, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-5386-4658-8, isbnType: New-2005
• authors:
  • Author Name: Chau- Wai Wong
    Affiliation: North Carolina State University, Raleigh, NC
    Author URL: https://ieeexplore.ieee.org/author/37085610089
    ID: 37085610089
    Order: 1
    Author Affiliations:
    • North Carolina State University, Raleigh, NC
  • Author Name: Adi Hajj-Ahmad
    Affiliation: North Carolina State University, Raleigh, NC
    Author URL: https://ieeexplore.ieee.org/author/37071414900
    ID: 37071414900
    Order: 2
    Author Affiliations:
    • North Carolina State University, Raleigh, NC
  • Author Name: Min Wu
    Affiliation: University of Maryland, College Park, MD
    Author URL: https://ieeexplore.ieee.org/author/37277237100
    ID: 37277237100
    Order: 3
    Author Affiliations:
    • University of Maryland, College Park, MD

Image Sensor

• sensor_type: CMOS
• resolution: 3024x2448 (iPhone 6s), 2448x2448 (iPhone 6, iPhone 5)
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

• shutter_speed: 1/60 (implied for 60Hz ENF)

Applications & Benefits

• cell_imaging: Enabled location-based forensic capabilities for law enforcement and security purposes by analyzing embedded electrical frequency signals in images.
• benefits: Facilitates geo-tagging for images without visible landmarks or GPS data.

Supporting Organizations

• supported_by: North Carolina State University, University of Maryland, GE Digital

Manuscript Details

• publication_date: 15-20 April 2018

Relevancy Score

• score: 10
• missing_fields:
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • power_consumption
  • noise

Processed JSON Filename

request_6337cf91-73a7-4c6c-8fb3-dbec40cf358f-invisible_geolocation_signature_in_a_single_image.json