Multi-scale image hashing using adaptive local feature extraction for robust tampering detection

Yan, Cai-Ping; Pun, Chi‐Man; Yuan, Xiaochen

doi:10.1016/j.sigpro.2015.10.027

Cited by 69 publications

(26 citation statements)

References 23 publications

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“…Content integrity of multimedia data has been tackled in the past from the perspective of manipulation prevention using watermarking, authentication and hashing [1,17,19,21]. Most of such work is aimed at detecting tampered data, especially images, and approaches to recover the original data.…”

Section: Related Workmentioning

confidence: 99%

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

Jaiswal¹,

Sabir²,

AbdAlmageed³

et al. 2017

Proceedings of the 25th ACM International Conference on Multimedia

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Real world multimedia data is o en composed of multiple modalities such as an image or a video with associated text (e.g. captions, user comments, etc.) and metadata. Such multimodal data packages are prone to manipulations, where a subset of these modalities can be altered to misrepresent or repurpose data packages, with possible malicious intent. It is, therefore, important to develop methods to assess or verify the integrity of these multimedia packages. Using computer vision and natural language processing methods to directly compare the image (or video) and the associated caption to verify the integrity of a media package is only possible for a limited set of objects and scenes. In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages. We construct a joint embedding of images and captions with deep multimodal representation learning on the reference dataset in a framework that also provides image-caption consistency scores (ICCSs). e integrity of query media packages is assessed as the inlierness of the query ICCSs with respect to the reference dataset. We present the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media packages from Flickr, which we make available to the research community. We use both the newly created dataset as well as Flickr30K and MS COCO datasets to quantitatively evaluate our proposed approach. e reference dataset does not contain unmanipulated versions of tampered query packages. Our method is able to achieve F 1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO, respectively, for detecting semantically incoherent media packages. ACM Reference format:

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Section: Related Workmentioning

confidence: 99%

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

Jaiswal¹,

Sabir²,

AbdAlmageed³

et al. 2017

Proceedings of the 25th ACM International Conference on Multimedia

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“…In recent years, a number of perceptual hash algorithms have been proposed to meet the requirements of different types of data [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Ahmed et al [2] propose a perceptual hash algorithm for image authentication, which uses a secret key to randomly modulate image pixels to create a transformed feature space.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is robust to a wide range of distortions and attacks, and it yields better identification performances under geometric attacks such as rotation attacks and brightness changes. Yan et al [5] propose a multi-scale image hashing method by using the location-context information of the features generated by adaptive and local feature extraction techniques. Cui et al [6] propose a hash algorithm for 3D images by selecting suitable Dual-tree complex wavelet transform coefficients to form the final hash sequence.…”

Section: Introductionmentioning

confidence: 99%

A Novel Perceptual Hash Algorithm for Multispectral Image Authentication

2018

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Abstract:The perceptual hash algorithm is a technique to authenticate the integrity of images. While a few scholars have worked on mono-spectral image perceptual hashing, there is limited research on multispectral image perceptual hashing. In this paper, we propose a perceptual hash algorithm for the content authentication of a multispectral remote sensing image based on the synthetic characteristics of each band: firstly, the multispectral remote sensing image is preprocessed with band clustering and grid partition; secondly, the edge feature of the band subsets is extracted by band fusion-based edge feature extraction; thirdly, the perceptual feature of the same region of the band subsets is compressed and normalized to generate the perceptual hash value. The authentication procedure is achieved via the normalized Hamming distance between the perceptual hash value of the recomputed perceptual hash value and the original hash value. The experiments indicated that our proposed algorithm is robust compared to content-preserved operations and it efficiently authenticates the integrity of multispectral remote sensing images.

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“…That has become a routine practice in many image hashing methods. Many previous schemes are either based on global [2][3][4][5] or local [6][7][8][9][10][11] features. In this paper, we propose a method to construct robust image hashing method using features of selected effective blocks.…”

Section: Introductionmentioning

confidence: 99%

Robust Image Hashing Based on Local Features for Image Authentication

Zhao¹,

Zhao²

2017

dtcse

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Abstract. In this paper, an image hashing method based on local features is proposed. At first the input image is pre-processed and divided into un-overlapped blocks. We choose several blocks as the effective blocks using SIFT features. Then color, texture and shape features of the selected blocks are extracted, connected and permuted to form the final hash. Experimental results show that this method is robust against most content-preserving attacks. Collision probability of this method is smaller than other methods. This method can also be used to detect tampering image.

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Multi-scale image hashing using adaptive local feature extraction for robust tampering detection

Cited by 69 publications

References 23 publications

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

A Novel Perceptual Hash Algorithm for Multispectral Image Authentication

Robust Image Hashing Based on Local Features for Image Authentication

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