Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing
Perceptual image hashing is used in a wide range of practical applications which include content image authentica- tion, digital watermarking, pattern recognition, computer vision and database fast duplicate image retrieval. Existing techniques are not well suited for the significant brightness and c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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FRUCT
2019-11-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://fruct.org/publications/fruct25/files/Zhu.pdf
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| _version_ | 1828382607580069888 |
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| author | Aleksei Zhuvikin Valery Korzhik |
| author_facet | Aleksei Zhuvikin Valery Korzhik |
| author_sort | Aleksei Zhuvikin |
| collection | DOAJ |
| description | Perceptual image hashing is used in a wide range of practical applications which include content image authentica- tion, digital watermarking, pattern recognition, computer vision and database fast duplicate image retrieval. Existing techniques are not well suited for the significant brightness and contrast corrections. The main point is that such manipulations can lead to information loss due to the histogram truncation in cases when pixel values are out of the dynamic range. In order to address the issue a novel technique is suggested. Cumulative histogram slices as a pivot for the subsequent image features calculations are used. The points of slicing are calculated in a way they are robust to content preserving manipulations such as brightness and contrast corrections. This approach allows one to handle situations when some of the content slices are lost due to the pixel value overflow. On the other hand, if one tampers image content within any existing slice it will then be detected by comparing the correspondent calculated and provided hash values. Experiment results show that the suggested method has sufficient sensitivity to detect image tampering whereas being tolerant to even significant brightness and contrast corrections. The memory consumption allows one to use the proposed method with the digital watermarking schemes. |
| first_indexed | 2024-12-10T04:34:57Z |
| format | Article |
| id | doaj.art-28b6b3e2f6b2432ea65aa5c73420895e |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-10T04:34:57Z |
| publishDate | 2019-11-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-28b6b3e2f6b2432ea65aa5c73420895e2022-12-22T02:02:01ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372019-11-0162225391397Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram SlicingAleksei Zhuvikin0Valery Korzhik1The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint-Petersburg, RussiaThe Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint-Petersburg, RussiaPerceptual image hashing is used in a wide range of practical applications which include content image authentica- tion, digital watermarking, pattern recognition, computer vision and database fast duplicate image retrieval. Existing techniques are not well suited for the significant brightness and contrast corrections. The main point is that such manipulations can lead to information loss due to the histogram truncation in cases when pixel values are out of the dynamic range. In order to address the issue a novel technique is suggested. Cumulative histogram slices as a pivot for the subsequent image features calculations are used. The points of slicing are calculated in a way they are robust to content preserving manipulations such as brightness and contrast corrections. This approach allows one to handle situations when some of the content slices are lost due to the pixel value overflow. On the other hand, if one tampers image content within any existing slice it will then be detected by comparing the correspondent calculated and provided hash values. Experiment results show that the suggested method has sufficient sensitivity to detect image tampering whereas being tolerant to even significant brightness and contrast corrections. The memory consumption allows one to use the proposed method with the digital watermarking schemes.https://fruct.org/publications/fruct25/files/Zhu.pdf perceptual image hashingdigital watermarkingcontent image authenticationcumulative histogram slicingbrightnesscontrast |
| spellingShingle | Aleksei Zhuvikin Valery Korzhik Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing Proceedings of the XXth Conference of Open Innovations Association FRUCT perceptual image hashing digital watermarking content image authentication cumulative histogram slicing brightness contrast |
| title | Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing |
| title_full | Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing |
| title_fullStr | Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing |
| title_full_unstemmed | Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing |
| title_short | Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing |
| title_sort | perceptual image hashing tolerant to brightness and contrast corrections method based on cumulative histogram slicing |
| topic | perceptual image hashing digital watermarking content image authentication cumulative histogram slicing brightness contrast |
| url | https://fruct.org/publications/fruct25/files/Zhu.pdf
|
| work_keys_str_mv | AT alekseizhuvikin perceptualimagehashingtoleranttobrightnessandcontrastcorrectionsmethodbasedoncumulativehistogramslicing AT valerykorzhik perceptualimagehashingtoleranttobrightnessandcontrastcorrectionsmethodbasedoncumulativehistogramslicing |