Perceptual Image Hashing Based on Virtual Watermark Detection

Khelifi, Fouad; Jiang, Jianmin

doi:10.1109/tip.2009.2038637

Cited by 95 publications

(39 citation statements)

References 31 publications

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“…• Statistic-based schemes (Khelifi & Jiang, 2010;Schneider & Chang, 1996;Venkatesan et al, 2000): This group of schemes extracts hash features by calculating the images statistics in the spacial domain, such as mean, variance, higher moments of image blocks and histogram.…”

Section: Perceptual Image Hashing Methods Classificationmentioning

confidence: 99%

Perceptual Image Hashing

Hadmi¹,

Puech²,

Said³

et al. 2012

Watermarking - Volume 2

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Section: Perceptual Image Hashing Methods Classificationmentioning

confidence: 99%

Perceptual Image Hashing

Hadmi¹,

Puech²,

Said³

et al. 2012

Watermarking - Volume 2

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“…This confirms the suitability of the identification distance for this particular application. The reason that TIRI exhibits good efficiency under geometric distortions is partly due to the fact that the DCT-based features are extracted from overlapping blocks which have been shown to offer more robustness than non overlapping blocks in [34]. It can be seen from Fig.…”

Section: B Identification Performancementioning

confidence: 99%

“…We acknowledge a related work on still images using feature points [33] where the system has been shown to deliver better authentication results when compared to transform-based hashing techniques (DWT and DCT). The identification results, however, have been outperformed by other techniques, including the waveletbased hashing technique, as demonstrated in [34] on attacked images. Furthermore, the system assumes that the tampered regions provide a mismatch of several extracted feature points.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Video Hashing for Content Identification and Authentication

Khelifi

Bouridane

2019

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions.

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“…Existing algorithms generally differ in two aspects: 1) whether particular features are required; 2) whether training is required. Perceptual hashing [9] mainly deals with the latter two applications. Corresponding algorithms are typically feature-dependent, and do not require training.…”

Section: Introductionmentioning

confidence: 99%

Supervised Multi-scale Locality Sensitive Hashing

Weng

Jhuo

Shi

et al. 2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

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LSH is a popular framework to generate compact representations of multimedia data, which can be used for content based search. However, the performance of LSH is limited by its unsupervised nature and the underlying feature scale. In this work, we propose to improve LSH by incorporating two elements -supervised hash bit selection and multi-scale feature representation. First, a feature vector is represented by multiple scales. At each scale, the feature vector is divided into segments. The size of a segment is decreased gradually to make the representation correspond to a coarse-to-fine view of the feature. Then each segment is hashed to generate more bits than the target hash length. Finally the best ones are selected from the hash bit pool according to the notion of bit reliability, which is estimated by bit-level hypothesis testing.Extensive experiments have been performed to validate the proposal in two applications: near-duplicate image detection and approximate feature distance estimation. We first demonstrate that the feature scale can influence performance, which is often a neglected factor. Then we show that the proposed supervision method is effective. In particular, the performance increases with the size of the hash bit pool. Finally, the two elements are put together. The integrated scheme exhibits further improved performance. * L.

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Perceptual Image Hashing Based on Virtual Watermark Detection

Cited by 95 publications

References 31 publications

Perceptual Image Hashing

Perceptual Image Hashing

Perceptual Video Hashing for Content Identification and Authentication

Supervised Multi-scale Locality Sensitive Hashing

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