/pdf/implementation-and-benchmarking-of-perceptual-image-hash-33ahqiaiwk.pdf

Implementation and Benchmarking of Perceptual Image Hash Functions

Unsolicited bulk e-mail, or SPAM, is a means to an end For virtually all such messages, the intent is to attract the recipient into entering a commercial transaction -- typically via a linked Web site While the prodigious infrastructure used to pump out billions of such solicitations is essential, the engine driving this process is ultimately the "point-of-sale" -- the various money-making "scams" that extract value from Internet users In the hopes of better understanding the business pressures exerted on spammers, this paper focuses squarely on the Internet infrastructure used to host and support such scams We describe an opportunistic measurement technique called spamscatter that mines emails in real-time, follows the embedded link structure, and automatically clusters the destination Web sites using image shingling to capture graphical similarity between rendered sites We have implemented this approach on a large real-time spam feed (over 1M messages per week) and have identified and analyzed over 2,000 distinct scams on 7,000 distinct servers

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Spamscatter: characterizing internet scam hosting infrastructure

Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination.

Robust Image Hashing With Ring Partition and Invariant Vector Distance

This paper designs an efficient image hashing with a ring partition and a nonnegative matrix factorization (NMF), which has both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and helps to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment, and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination.

Robust Perceptual Image Hashing Based on Ring Partition and NMF

Video fingerprints are feature vectors that uniquely characterize one video clip from another. The goal of video fingerprinting is to identify a given video query in a database (DB) by measuring the distance between the query fingerprint and the fingerprints in the DB. The performance of a video fingerprinting system, which is usually measured in terms of pairwise independence and robustness, is directly related to the fingerprint that the system uses. In this paper, a novel video fingerprinting method based on the centroid of gradient orientations is proposed. The centroid of gradient orientations is chosen due to its pairwise independence and robustness against common video processing steps that include lossy compression, resizing, frame rate change, etc. A threshold used to reliably determine a fingerprint match is theoretically derived by modeling the proposed fingerprint as a stationary ergodic process, and the validity of the model is experimentally verified. The performance of the proposed fingerprint is experimentally evaluated and compared with that of other widely-used features. The experimental results show that the proposed fingerprint outperforms the considered features in the context of video fingerprinting.

Robust Video Fingerprinting for Content-Based Video Identification

Robust signal hashing defines a feature vector that characterizes the signal, independently of "nonsignificant" distortions of its content. When dealing with images, the considered distortions are typically due to compression or small geometrical manipulations. In other words, robustness means that images that are visually indistinguishable should produce equal or similar hash values. To discriminate image contents, a hash function should produce distinct outputs for different images. Our paper first proposes a robust hashing algorithm for still images. It is based on radial projection of the image pixels and is denoted the Radial hASHing (RASH) algorithm. Experimental results provided on the USC-SIPI dataset reveal that the proposed RASH feature vector is more robust and provides much stronger discrimination than a conventional histogram-based feature vector. The RASH vector appears to be a good candidate to build indexing algorithms, copy-detection systems, or content-based authentication mechanisms. To take benefit from the RASH vector capabilities, video content is summarized into key frames, each of them characterizing a video shot and described by its RASH vector. The resulting video hashing system works in real time and supports most distortions due to common spatial and temporal video distortions.

Robust video hashing based on radial projections of key frames

Robust image hashing defines a feature vector that characterizes the image, independently of non-significant distortions of its content. As a consequence, the comparison between robust image hash vectors is able to indicate whether the corresponding images are equivalent or not, independently of visually non-significant distortions due for example to compression or re-sampling. We define a robust image hash based on radial projections of the image pixels. Specifically, our proposed radial hASH (RASH) considers moments of different orders to describe the luminance pdf of the pixels encountered on a set of lines articulated around the center of the image. In short, each RASH component is defined based on the moment of the pixels belonging to a specific line. Our paper provides a careful analysis of the robustness and discriminating capabilities of the RASH vectors computed based on different moment orders. As a first contribution, it demonstrates that the second order moment, i.e. the variance of the pixels on a line, allows for optimal trade-offs between the robustness and the discriminating capabilities of the resulting RASH vector. As a second contribution, extensive simulations prove that a decision engine based on RASH vectors cross-correlations is able to successfully identify pairs of equivalent or distinct images. Bottom line, the RASH assets are a low computational complexity, a strong robustness to both filtering and geometrical distortions, and a risk of collision that is estimated to less than 10 per million of images.

Robust image hashing based on radial variance of pixels

This paper presents a segmentation method combining an active contour approach with a watershed pre-segmentation. Segmentation is performed on two modalities: one being the color of the regions, the other one being the change due to motion. On the one hand, watershed methods are very efficient to provide an oversegmentation of homogeneous color regions. On the other hand, active contours methods are efficient to obtain a smooth segmentation. We apply in this paper an active contour method to segment the change detection mask. The incertitude on the motion induces artifacts on the resulting contours. Our original contribution consists in using the intra-image watershed segmentation to correct the contours. To reach such a goal, we construct a distance map from the oversegmentation of the homogeneous color region. The active contour is then corrected to better fit to the real object by introducing a specific term based on this distance map in the evolving equation of the active contour.

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Multimodal segmentation combining active contours and watersheds

In this paper we address the problem of video segmentation. We propose a new method which combines an active contour and a watershed presegmentation. We first apply active contours to track moving objects with a basic motion criterion. Then, we eliminate the variation due to motion on the object boundaries by attracting the curve to the nearest boundaries of a watershed oversegmentation. We constrain the curve length to smooth the propagation driven by the distance to the watershed boundaries. The proposed method improves active contour segmentation and gives smoother results than watershed segmentation

Watershed-driven active contours for moving object segmentation

Lack of temporal coherence in video segmentation algorithms often leads to flickering or to discrepancies on the segmentation mask boundaries. Active contour video segmentation algorithms can lead to very smooth segmentation masks when they are defined in three dimensions (two spatial and one temporal). In this paper, we will use an active contour method to attract an active surface toward a non-smoothed segmentation mask boundary. This active surface will produce a new segmentation mask which will be smoother than the first one. To achieve this task, we introduce a new region-based term for active contour segmentation in the variational framework. This term attracts the evolving curve to a reference contour. The energy criterion and the evolution equation are defined in n-dimension and we investigate the particular case of two regions. The use of active surfaces to smooth video segmentation masks appears to be a very powerful tool for video postprocessing. The resulting masks are smoother than the original ones, the discrepancies of the segmentation masks are removed and the flickering on the boundary of the segmentation masks are considerably reduced.

C. De Roover

Papers

Robust video hashing based on radial projections of key frames

Robust image hashing based on radial variance of pixels

Multimodal segmentation combining active contours and watersheds

Watershed-driven active contours for moving object segmentation

Active Contour Attracted by a Reference Contour: A Region-Based Approach