The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Willem Jonker - One of the best experts on this subject based on the ideXlab platform.

  • SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

  • ICASSP - SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company’s legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

Christoph Bösch - One of the best experts on this subject based on the ideXlab platform.

  • SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

  • ICASSP - SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company’s legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

Andreas Peter - One of the best experts on this subject based on the ideXlab platform.

  • SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

  • ICASSP - SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company’s legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

  • WIFS - Privacy-preserving architecture for Forensic Image recognition
    2012 IEEE International Workshop on Information Forensics and Security (WIFS), 2012
    Co-Authors: Andreas Peter, Thomas Hartmann, Sascha Müller, Stefan Katzenbeisser
    Abstract:

    Forensic Image recognition is an important tool in many areas of law enforcement where an agency wants to prosecute possessors of illegal Images. The recognition of illegal Images that might have undergone human imperceptible changes (e.g., a JPEG-recompression) is commonly done by computing a perceptual Image hash function of a given Image and then matching this hash with perceptual hash values in a database of previously collected illegal Images. To prevent privacy violation, agencies should only learn about Images that have been reliably detected as illegal and nothing else. In this work, we argue that the prevalent presence of separate departments in such agencies can be used to enforce the need-to-know principle by separating duties among them. This enables us to construct the first practically efficient architecture to perform Forensic Image recognition in a privacy-preserving manner. By deriving unique cryptographic keys directly from the Images, we can encrypt all sensitive data and ensure that only illegal Images can be recovered by the law enforcement agency while all other information remains protected.

  • Privacy-preserving architecture for Forensic Image recognition
    2012 IEEE International Workshop on Information Forensics and Security (WIFS), 2012
    Co-Authors: Andreas Peter, Thomas Hartmann, Sascha Müller, Stefan Katzenbeisser
    Abstract:

    Forensic Image recognition is an important tool in many areas of law enforcement where an agency wants to prosecute possessors of illegal Images. The recognition of illegal Images that might have undergone human imperceptible changes (e.g., a JPEG-recompression) is commonly done by computing a perceptual Image hash function of a given Image and then matching this hash with perceptual hash values in a database of previously collected illegal Images. To prevent privacy violation, agencies should only learn about Images that have been reliably detected as illegal and nothing else. In this work, we argue that the prevalent presence of separate departments in such agencies can be used to enforce the need-to-know principle by separating duties among them. This enables us to construct the first practically efficient architecture to perform Forensic Image recognition in a privacy-preserving manner. By deriving unique cryptographic keys directly from the Images, we can encrypt all sensitive data and ensure that only illegal Images can be recovered by the law enforcement agency while all other information remains protected.

Pieter Hartel - One of the best experts on this subject based on the ideXlab platform.

  • SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company's legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

  • ICASSP - SOFIR: Securely outsourced Forensic Image recognition
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Christoph Bösch, Andreas Peter, Pieter Hartel, Willem Jonker
    Abstract:

    Forensic Image recognition tools are used by law enforcement agencies all over the world to automatically detect illegal Images on confiscated equipment. This detection is commonly done with the help of a strictly confidential database consisting of hash values of known illegal Images. To detect and mitigate the distribution of illegal Images, for instance in network traffic of companies or Internet service providers, it is desirable to outsource the recognition of illegal Images to these companies. However, law enforcement agencies want to keep their hash databases secret at all costs as an unwanted release may result in misuse which could ultimately render these databases useless. We present SOFIR, a tool for the Secure Outsourcing of Forensic Image Recognition allowing companies and law enforcement agencies to jointly detect illegal network traffic at its source, thus facilitating immediate regulatory actions. SOFIR cryptographically hides the hash database from the involved companies. At fixed intervals, SOFIR sends out an encrypted report to the law enforcement agency that only contains the number of found illegal Images in the given interval, while otherwise keeping the company’s legal network traffic private. Our experimental results show the effectiveness and practicality of our approach in the real-world.

Fernando Pérez-gonzález - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - An SVD approach to Forensic Image resampling detection
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: David Vázquez-padín, Pedro Comesaña, Fernando Pérez-gonzález
    Abstract:

    This paper describes a new strategy for Image resampling detection whenever the applied resampling factor is larger than one. Delving into the linear dependencies induced in an Image after the application of an upsampling operation, we show that interpolated Images belong to a subspace defined by the interpolation kernel. Within this framework, by computing the SVD of a given Image block and a measure of its degree of saturated pixels per row/column, we derive a simple detector capable of discriminating between upsampled Images and genuine Images. Furthermore, the proposed detector shows remarkable results with blocks of small size and outperforms state-of-the-art methods.

  • An SVD approach to Forensic Image resampling detection
    2015 23rd European Signal Processing Conference (EUSIPCO), 2015
    Co-Authors: David Vázquez-padín, Pedro Comesaña, Fernando Pérez-gonzález
    Abstract:

    This paper describes a new strategy for Image resampling detection whenever the applied resampling factor is larger than one. Delving into the linear dependencies induced in an Image after the application of an upsampling operation, we show that interpolated Images belong to a subspace defined by the interpolation kernel. Within this framework, by computing the SVD of a given Image block and a measure of its degree of saturated pixels per row/column, we derive a simple detector capable of discriminating between upsampled Images and genuine Images. Furthermore, the proposed detector shows remarkable results with blocks of small size and outperforms state-of-the-art methods.