The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

Media objects are transformed into active, connected objects via identifiers embedded into them or their containers. In the context of a user's playback experience, a decoding process extracts the identifier from a media object and possibly additional context information and forwards it to a server. The server, in turn, maps the identifier to an action, such as returning metadata, re-directing the request to one or more other servers, requesting information from another server to identify the media object, etc. The linking process applies to broadcast objects as well as objects transmitted over networks in streaming and compressed file formats.

Connected audio and other media objects

Imagine the following situation. You’re in your car, listening to the radio and suddenly you hear a song that catches your attention. It’s the best new song you have heard for a long time, but you missed the announcement and don’t recognize the artist. Still, you would like to know more about this music. What should you do? You could call the radio station, but that’s too cumbersome. Wouldn’t it be nice if you could push a few buttons on your mobile phone and a few seconds later the phone would respond with the name of the artist and the title of the music you’re listening to? Perhaps even sending an email to your default email address with some supplemental information. In this paper we present an audio fingerprinting system, which makes the above scenario possible. By using the fingerprint of an unknown audio clip as a query on a fingerprint database, which contains the fingerprints of a large library of songs, the audio clip can be identified. At the core of the presented system are a highly robust fingerprint extraction method and a very efficient fingerprint search strategy, which enables searching a large fingerprint database with only limited computing resources.

https://ismir2002.ismir.net/proceedings/02-FP04-2.pdf

A Highly Robust Audio Fingerprinting System.

Hashes are short summaries or signatures of data files which can be used to identify the file. The disclosed method generates robust hashes for multimedia content, for example, audio clips. The audio clip is divided into successive (preferably overlapping) frames. For each frame, the frequency spectrum is divided into bands. A robust property of each band (e.g. energy) is computed and represented by a respective hash bit. An audio clip is thus represented by a concatenation of binary hash words, one for each frame. To identify a possibly compressed audio signal, a block of hash words derived therefrom is matched by a computer with a large database. Such matching strategies are also disclosed. In an advantageous embodiment, the extraction process also provides information as to which of the hash bits are the least reliable. Flipping these bits considerably improves the speed and performance of the matching process.

Generating and matching hashes of multimedia content

Nowadays most audio content identification systems are based on watermarking technology. In this paper we present a different technology, referred to as robust audio hashing. By extracting robust features and translating them into a bit string, we get an object called a robust hash. Content can then be identified by comparing hash values of a received audio clip with the hash values of previously stored original audio clips. A distinguishing feature of the proposed hash scheme is its ability to extract a bit string for every so many milliseconds. More precisely, for every windowed time interval a hash value of 32 bits is computed by thresholding energy differences of several frequency bands. A sequence of 256 hash values, corresponding to approximately 3 seconds of audio, can uniquely identify a song. Experimental results show that the proposed scheme is robust against severe compression, but bit errors do occur. This implies that searching and matching is a non-trivial task for large databases. For instance, a brute force search approach is already prohibitive for databases containing hash values of more than 100 songs. Therefore we propose a scheme that exploits a structured search algorithm that allows searching databases containing over 100,000 songs.

Robust Audio Hashing for Content Identification

This paper presents the concept of video fingerprinting as a tool for video identification. As such, video fingerprinting is an important tool for persistent identification as proposed in MPEG-21. Applications range from video monitoring on broadcast channels to filtering on peer-to-peer networks to meta-data restoration in large digital libraries. We present considerations and a technique for (i) extracting essential perceptual features from moving image sequences and (ii) for identifying any sufficiently long unknown video segment by efficiently matching the fingerprint of the short segment with a large database of pre-computed fingerprints.

Feature Extraction and a Database Strategy for Video Fingerprinting

This invention relates to a device and a method of generating a first and a second fingerprint (102,104) usable for synchronisation of at least two signals (101,103) and corresponding method and device for synchronising two or more signals. A fingerprint pair is generated on the basis of a segment of a first signal e.g. an audio signal and of a segment of a second signal e.g. a video signal at each synchronisation time point. The generated fingerprint pair(s) are stored in a database (203) and communicated or distributed to a synchronisation device (303). During synchronisation, fingerprint(s) of the audio signal and fingerprint(s) of the video signal to be synchronised are generated and matched against fingerprints in the database. When a match is found, the fingerprints also determine the synchronisation time point, which is used to synchronise the two signals. In this way, a simple, reliable and efficient way of synchronising at least two signals is obtained. Further, this is enabled without modifying either the first and second signal (or subsequent signals). The signals may even be distorted or changed to some extent while still enabling synchronisation.

Method and Device for Generating and Detecting Fingerprints for Synchronizing Audio and Video

The invention relates to methods and apparatus for matching a query data object with a candidate data object by esetracting and comparing fingerprints of said data objects. In an embodiment of the invention apparatus comprising a fingerprint extraction module (110), a fingerprint matching module (210), a statistical module (120) and an identification module is provided. The fingerprint extraction module (110) receives an information signal forming part of a query object and constructs a query fingerprint. The fingerprint matching module (210) compares the query fingerprint to candidates stored in a database (215) to find at least on potentially best matching candidate. Meanwhile, the statistical module determines a statistical model of the query fingerprint so as to, for instance, determine the statistical distribution of certain information inside the query fingerprint. The threshold determiner (120) is arranged, on the basis of the distribution of the query fingerprint to derive an adaptive threshold distance T within which the query fingerprint and a potentially best matching candidate may be declared similar by the identification module (130). By setting a threshold which ma depand on satatistical data derived from the query and/or candidate fingerprint , an improved false acceptance rate F.A.R. may be achieved.

Job C. Oostveen

Papers

Generating and matching hashes of multimedia content

Robust Audio Hashing for Content Identification

Feature Extraction and a Database Strategy for Video Fingerprinting

Method and Device for Generating and Detecting Fingerprints for Synchronizing Audio and Video

Matching data objects by matching derived fingerprints