Topic
Feature hashing
About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.
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07 Aug 2002
TL;DR: The difficulty of recovering an input from an MLP network hashed output is presented and important features of good hash algorithms such as resistance to birthday attacks and collision free hashing are explored with regard to theMLP network.
Abstract: In this paper, the applicability of using a multilayer-perceptron (MLP) network as a possible hash algorithm is investigated. The difficulty of recovering an input from an MLP network hashed output is presented. Important features of good hash algorithms such as resistance to birthday attacks and collision free hashing are explored with regard to the MLP network. Possible advantages of using such an arrangement over existing hash algorithms are mentioned.
14 citations
26 Sep 2006
TL;DR: A wavelet-based robust database hashing scheme is analyzed with respect to its resilience against image modifications and hostile attacks and a method to construct a forgery is presented.
Abstract: A wavelet-based robust database hashing scheme is analyzed with respect to its resilience against image modifications and hostile attacks. A method to construct a forgery is presented and possible countermeasures are discussed.
14 citations
TL;DR: A 2D-based hashing method which could fast extract the binary feature of samples is proposed and successfully applied into tracking model by some details and an effective and suitable learning model to update hash functions at every frame is designed.
14 citations
TL;DR: This work proposes an image hashing scheme based on Low-Rank and Sparse Representation that is robust to content preserving modifications and has better image recovery performance compared with existing hashing schemes.
Abstract: Multimedia hash is an effective solution to image authentication and tampering identification. We propose an image hashing scheme based on Low-Rank and Sparse Representation. Low-Rank Representation is applied to the attacked image to obtain image feature matrix and error matrix. Then the properties of dimension reduction and tampering recovery inherent in Low-Rank Representation and Compressive Sensing are exploited for hash design. We use Compressive Sensing to recover the primary feature of image. Furthermore we use Low-Rank Representation to recover the image from tampering. Thanks to the error correction and structure recover capabilities of Low-Rank Representation, experiments reveal that our proposed hashing scheme is robust to content preserving modifications and has better image recovery performance compared with existing hashing schemes.
14 citations
TL;DR: Experimental validation illustrates that the proposed fusion methods for combining features in multimodal biometrics advances the recognition performance significantly.
Abstract: This paper presents a new feature-level information fusion mechanism based on shuffle coding, called shuffle coding-based feature-level fusion (SC-FLF), for personal authentication. Our approach (SC-FLF) aims at constructing an information fusion mechanism to integrate features from the same or different feature spaces in which the ranges of feature values from different traits differ largely. In this mechanism, the shuffle-coding operator includes dimension adjustment, feature standardization, and fusion coding. This paper addresses two distinct methods, such as feature scaling and hashing, to standardize the range of independent features of data. The shuffle encoder of the SC-FLF in Method 1 uses a feature scaling and the resulting binary code represents the distance between a set of normalized feature values with 2’s complement. On the other hand, in Method 2, the shuffle encoder of the SC-FLF with hashing uses a projection framework for maximizing the features on a hyperplane and then quantize...
14 citations