High capacity reversible data hiding in encrypted images using multi-MSB data hiding mechanism with elliptic curve cryptography

Deshmukh, Priyanka V.; Thakare, V. M.; Kapse, Avinash S.

doi:10.1007/s11042-023-14683-9

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Unlike VRAE, the RRBE research requires a pre-processing operation to be completed prior to image encryption to release the embedding space, or more precisely, to create space in the plaintext domain [20][21][22][23][24][25][26][27][28][29][30][31]. Ma et al [20] proposed the first RRBE solution for dealing with capacity and visual quality issues in VRAE.…”

Section: Introductionmentioning

confidence: 99%

Reversible data hiding in encrypted images with multi-prediction and adaptive Huffman encoding

Ren

Yue

2023

Preprint

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With the rapid development of network technology and the massive accumulation of user data, huge amounts of data are being rapidly generated and shared on the network, while the problems of unauthorized data access and misuse continue to emerge. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data into encrypted content and accurately extracts the embedded data without affecting the original content. However, the amount of embedded protected information has always been one of the major constraints on the performance and application of RDHEI. Currently, the main approaches to improve the net embedding capacity of RDHEI are to increase the total embedding capacity or to reduce the length of the auxiliary information to be embedded. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the total embedding capacity, we use the MED + GAP predictor to generate the label map data of non-reference pixels before image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels to reduce the embedding length of the auxiliary information used for extraction and recovery. Experimental results show that with MED + GAP predictor and adaptive Huffman coding, the proposed method achieves a higher embedding capacity than other recent methods while ensuring security and reversibility.

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Section: Introductionmentioning

confidence: 99%

Reversible data hiding in encrypted images with multi-prediction and adaptive Huffman encoding

Ren

Yue

2023

Preprint

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“…By preserving the auxiliary and encoded information, the carrier image can be recovered losslessly after data extraction. Zhou and Chen 16 proposed a novel VRAE-based algorithm that combines block classification and multi-layer processing to fully explore the redundant space.In contrast, RRBE research requires a pre-processing operation before image encryption to free the embedding space, or more precisely, to create space in the plaintext domain [20][21][22][23][24][25][26][27][28][29][30][31][32] , as shown in Fig. 2.…”

mentioning

confidence: 99%

“…In contrast, RRBE research requires a pre-processing operation before image encryption to free the embedding space, or more precisely, to create space in the plaintext domain [20][21][22][23][24][25][26][27][28][29][30][31][32] , as shown in Fig. 2.…”

mentioning

confidence: 99%

Reversible data hiding in encrypted images with multi-prediction and adaptive huffman encoding

Ren,

Bai,

Chen

et al. 2023

Sci Rep

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With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data in an encrypted domain and accurately extracts the embedded data without affecting the original content. However, the amount of embedded data has been one of the major limitations in the performance and application of RDHEI. Currently, the main approaches to improve the capacity of RDHEI are either to increase the overall capacity or to reduce the length of the auxiliary information. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the overall capacity, we propose a multi-prediction, called MED+GAP predictor, to generate the label map data of non-reference pixels prior to image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels in order to reduce the embedding length of the auxiliary information used for the extraction and recovery. Experiments show that the proposed method with MED+GAP predictor and adaptive Huffman coding improves 0.052 bpp, 0.023 bpp, and 0.047 bpp on average over the other state-of-the-art methods on the BOSSBase, BOWS-2, and UCID datasets, respectively, while maintaining security and reversibility.

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A Survey on Helmet Detection by CNN Algorithm

Kapse,

Shreevamshi,

et al. 2023

ITM Web Conf.

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Accidents by not wearing helmet infractions are now a big problem for most emerging nations in the modern, changing world. Both the number of vehicles on the road and the number of traffic law offences are growinPly. Not wearing the helmet enforcement has always had a difficult and risky job. Despite the fact that traffic control has evolved into Due to the variety of plate types, various sizes, rotations, and uneven illumination during picture capture, automating the process is a particularly difficult task. The main goal of this project is to properly and efficiently control thehe accidents because of not wearing Helmet. The suggested model incorporates a computer-based camera-based automated system for video recording. In order to identify number plates more quickly and easily, the project offers Automatic Number Plate Recognition (ANPR) approaches as well as additional image-manipulation methods for plate localization and character recognition. The SMS-based module is used to alert the owners of the vehicles about their traffic rule violations after identifying the car number from the number plate. To trace the report, an additional SMS is sent to the Regional Transport Office (RTO).

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High capacity reversible data hiding in encrypted images using multi-MSB data hiding mechanism with elliptic curve cryptography

Cited by 6 publications

References 29 publications

Reversible data hiding in encrypted images with multi-prediction and adaptive Huffman encoding

Reversible data hiding in encrypted images with multi-prediction and adaptive Huffman encoding

Reversible data hiding in encrypted images with multi-prediction and adaptive huffman encoding

A Survey on Helmet Detection by CNN Algorithm

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