scholarly journals Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Du ◽  
Zhen Chen ◽  
Yongzhen Ke
Keyword(s):  
Semantic Information ◽  
Semantic Content ◽  
Tamper Detection ◽  
Image Hashing ◽  
High Quality ◽  
Multiple Features ◽  
Memory Efficiency ◽  
Benchmark Datasets ◽  
Perceptual Hashing ◽  
Hashing Algorithm

Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

scholarly journals An Image Hashing Algorithm for Authentication with Multi-Attack Reference Generation and Adaptive Thresholding

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 227
Author(s):  
Ling Du ◽  
Zehong He ◽  
Yijing Wang ◽  
Xiaochao Wang ◽  
Anthony T. S. Ho
Keyword(s):  
Similarity Measures ◽  
Image Authentication ◽  
Adaptive Thresholding ◽  
Image Hashing ◽  
Memory Efficiency ◽  
Distorted Version ◽  
Benchmark Datasets ◽  
Information Set ◽  
Threshold Setting ◽  
Hashing Algorithm

Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

scholarly journals Linguistic Temporality, Logical Meaning and Narrative Perspectives: Adverbs /zai/ and /you/ in Modern Standard Chinese

2011 ◽  
Vol 1 (2) ◽  
pp. 25-38
Author(s):  
Jens KARLSSON
Keyword(s):  
Semantic Information ◽  
Semantic Content ◽  
Conceptual Analogy ◽  
Semantic Encoding ◽  
Standard Chinese ◽  
Decompositional Analysis ◽  
Modern Standard ◽  
Temporal Adverbs

In this paper is presented an inquiry into some aspects of the meaning and usage of two temporal adverbs zai (再) and you (又) in Modern Standard Chinese. A decompositional analysis of the semantic encoding of the adverbs is conducted, aiming to better explain their recorded differences in usage. First, a sketch of some of the fundamental features of linguistic temporality is provided in order to model the structure of temporal semantic information encoded in the adverbs. Non-temporal (logical) meaning such as assertion and inference is also shown to be an important aspect of the semantic content of the adverbs. Adverbs zai and you are shown to encode the same semantic content except for a difference in viewpoint; the first being prospective, the second retrospective. Concrete linguistic examples reflecting the intrinsic semantic encoding of the adverbs are raised and discussed. It is then argued that through combining the decompositional analysis with ideas concerning conceptual analogy, some issues raised by Lu and Ma (1999) regarding the usage of zai and you in past and future settings may be resolved.

Robust Hash for Detecting and Localizing Image Tampering

2010 ◽  
Vol 439-440 ◽  
pp. 1018-1023
Author(s):  
De Long Cui ◽  
Yong Fu Liu ◽  
Jing Long Zuo
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Image Hashing ◽  
Post Processing ◽  
Feature Extracting ◽  
Image Tampering ◽  
Secret Keys ◽  
Robust Image ◽  
Hashing Algorithm ◽  
Robust Image Hashing

In order to improve the sensitive to illegal manipulations of image hashing, a novel robust image hashing algorithm based on fractional Fourier transform (FRFT) for detecting and localizing image tampering is proposed in this paper. The framework of generating an image hashing includes three steps: preprocessing, feature extracting and post processing. The robust hashing sequence is obtained by FRFT coefficients of image blocks. The security of proposed algorithm is totally depended on the orders of FRFT which are saved as secret keys. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as JPEG compression, mid-filtering, and rotation, more importantly the tampered place can be identified accurately.

scholarly journals Benchmark datasets of immune receptor-epitope structural complexes

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Swapnil Mahajan ◽  
Zhen Yan ◽  
Martin Closter Jespersen ◽  
Kamilla Kjærgaard Jensen ◽  
Paolo Marcatili ◽  
...  
Keyword(s):  
Epitope Prediction ◽  
Structural Quality ◽  
Sequence Information ◽  
High Quality ◽  
Immune Receptor ◽  
Prediction Tools ◽  
Specific Epitope ◽  
Benchmark Datasets ◽  
Benchmark Performance ◽  
New Generation

Abstract Background The development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets. Results To address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need. Conclusions SCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.

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