Rotation invariant image authentication using Haralick features

Alice, K.; Ramaraj, N.; Rajagopalan, Sridhar

doi:10.1007/s11042-019-07750-7

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…It is observed that more than 96% of all the hash pairs have a correlation coefficient value of above .95. This result is similar to the results of using all features [1].…”

Section: Resultssupporting

confidence: 88%

“…Robustness is a measure of tolerance of the authentication system to content preserving manipulation. To measure the robustness of the proposed system a set of 5 standard images as shown in Figure 7 is taken.The result of Correlation Coefficient for various attacks for a set of 5 standard image processing images is graphically shown in Fig 8(a) -(f) and a comparison for the same with system using all Haralick features [1] is also shown. It is observed that the Correlation Coefficient S is above .95 for all types of attack listed in Table 2 except for rotation which is above .93.…”

Section: Robustnessmentioning

confidence: 99%

“…The average run time required to generate hash code for 200 different images is compared with [22], [25], [26] and is shown in Table 6. The bold text in Table 6 represents optimum results.The average run time of [25] is 0.437optimum as it uses only one low complexity entropy as feature for describing images and for ours it is 0.739.The average run time of optimized Haralick features isimproved over all Haralick features [1] because of feature selection. Considering the space complexity in terms of hash storage, the hash length proposed system is optimum with only 320 bit.…”

Section: Tamper Detection and Localizationmentioning

confidence: 99%

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Robust Image Authentication Using Optimized Haralick Features Based on Genetic Algorithm

Alice¹

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Recent development in multimedia data increases the role of digital images in many applications. As a result,content based image authentication with an aid of feature extraction techniques has a great impact. It suffers a serious drawback of increased computational complexity due to the availability of irrelevant and interdependent features, which contains no useful information about the image. In the proposed authentication system to decrease the complexity, not all haralick features are used but only the most influencing features that contain critical information about the image is used in hash generation. Feature selection based on chaotic genetic feature selection optimization algorithm is used to optimize the haralick features without compromising the accuracy of the system. A comparison on the system performance with all the features and with optimized features is performed. Experiment results shows resultsusing optimized features are similar to the results of using all the features..

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“…It is observed that more than 96% of all the hash pairs have a correlation coefficient value of above .95. This result is similar to the results of using all features [1].…”

Section: Resultssupporting

confidence: 88%

Section: Robustnessmentioning

confidence: 99%

Section: Tamper Detection and Localizationmentioning

confidence: 99%

See 1 more Smart Citation

Robust Image Authentication Using Optimized Haralick Features Based on Genetic Algorithm

Alice¹

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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“…This method has promising robustness, but its discrimination is not desirable yet. Based on the RP technique, Alice et al [49] used Haralick features to devise a hashing method for authentication. Similarly, Khelaifi and He [50] used RP and fractal image coding to design a hashing method.…”

Section: Related Workmentioning

confidence: 99%

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang

Tang

Huang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant lowdimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

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“…It creates a tabular form of probabilities. In this paper, we have used 14 Haralick texture features [3,42]. These features are calculated using the gray level co-occurrence matrix (GLCM).…”

Section: Haralick and Lbpmentioning

confidence: 99%

A novel feature based algorithm for soil type classification

Uddin

Hassan

2022

Complex Intell. Syst.

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Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment sectors. In Bangladesh, the population is increasing rapidly and at the same time, the total cultivable land is decreasing significantly. To ensure maximum crop production using the limited land resources, it is essential to identify and select the appropriate type of soil because different crops need different soil types. Currently, there are two types of methods available to determine the soil type, namely chemical and image analysis. Although the first one is accurate, it is expensive and time consuming. On the other hand, image based soil classification is much cheaper and faster but its accuracy level is low. In this study, we present a novel feature based algorithm that combines quartile histogram oriented gradients (Q-HOG), most frequent $$\varphi $$ φ -Pixels and a new feature selection method for classifying soil types. We have used four machine learning algorithms and evaluated the performance with different sets of features. We have also compared our work with two prominent and recent works on image-based soil classification systems. The experimental results show that the performance of our proposed method in terms of four standard evaluation metrics, namely accuracy, precision, F1_score, and recall scores are higher than the existing image-based soil classification systems.

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Rotation invariant image authentication using Haralick features

Cited by 8 publications

References 13 publications

Robust Image Authentication Using Optimized Haralick Features Based on Genetic Algorithm

Robust Image Authentication Using Optimized Haralick Features Based on Genetic Algorithm

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

A novel feature based algorithm for soil type classification

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