Video mosaic watermarking using plasma key

Hassan, Nidaa Flaih; Ali, Akbas Ezaldeen; Aldeen, Teaba Wala; Al-Adhami, Ayad

doi:10.11591/ijeecs.v22.i2.pp619-628

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Symmetric encryption methods use only one key for encryption as well as decryption [12]. A single key usage for encryption and decryption a straightforward operation and that's why it's knows as "symmetric" [13]. Symmetric method is considered a great technique is the simplicity of its process where it lies in a single key usage for encryption and decryption [14].…”

Section: Symmetric and Asymmetric Encryptionmentioning

confidence: 99%

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

Hussien

Khairi

2023

Int. J. Interact. Mob. Technol.

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I n these days of technology, the usage of images has become increasingly high especially now that almost everyone has access to internet. Also, image helps us to learn, grabs our attention, explains complicated concepts as well as inspires us. Sharing these images is essential and therefore image encryption algorithms are proposed to secure the transmission of these image from many types of attacks such as Man-in-the-middle attack (MITM). In this paper, we proposed a hybrid security system that consist of Elliptic Curve Cryptography (ECC) and Advanced Encryption System (AES). Where ECC is used to generate private/public keys and AES is for encryption and decryption of the image using the ECC generated keys. The system works as follows; it begins by inserting the image to be encrypted which is “lena.png” of size 256*256 and passing it to AES Algorithm along with the generated public key. Then, in AES algorithm, the public key is hashed then used to encrypt the image with AES encryption algorithm. On the other hand, the decryption algorithm works as follows; inserting the encrypted image then calculating the decryption key to use it to decrypt the image using AES decryption algorithm. Finally, our experimental results shows that the National Institute of Standards and Technology (NIST) test shows that the ECC generated keys have better randomness than using only AES generated keys. Also, the encrypted image histogram show that the image pixels values are well distributed across all three channels R, G and B. This shows that the hybrid system is a step further to get a more secure image encryption system against attacks with the generated ECC keys. To get further, Logistic chaotic map has been used to encrypt images for comparison purposes with AES and ECC generated images in terms of randomness, security and histogram

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Section: Symmetric and Asymmetric Encryptionmentioning

confidence: 99%

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

Hussien

Khairi

2023

Int. J. Interact. Mob. Technol.

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“…, ℎ 𝑀−1 (𝑡) on the [0,1] are orthogonality and basis functions. If 𝑟 = 2 −𝑘 , 𝑠 = (𝑟(2𝑢 − 1)) and transform 𝑥 = 𝑟(2𝑟 −1 𝑡), replacing parameters r, s with a variable in (3) is obtained (2). DHWT ℎ 𝑢,𝑣 (𝑡) = ℎ(𝑘, 𝑢, 𝑣, 𝑡) includes four parameters, 𝑘 ∈ ℤ + , 𝑡 independent variable in [0,1], and 𝑣 is the degree of the Hermite polynomials:…”

Section: Discrete Hermite Wavelet Transformmentioning

confidence: 99%

The effectiveness of discrete hermite wavelet filters technique in digital image watermarking

Abduldaim¹,

Abdulrahman²,

Tahir³

2022

IJEECS

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<span>In this work, new discrete wavelets were derived Hermite polynomials for obtained discrete hermite wavelet transformation (DHWT), and their efficiency for use in image processing is demonstrated by proving the realization of important theorems. Moreover, the role of the new and proposed waveforms in their effective effect in placing the watermark with the color image is clarified, and a program was created using MATLAB software by creating a subprogram for constructing the new wavelet and proving its efficiency with an analytic image. The process is repeated using DHWT to analyze the image. The color image has been subjected to various attacks after which the watermark is retrieved from the image after comparing it with the proposed algorithm and it has proven its power faster and better than the previously suggested methods. The final conclusion shows that using new wavelets DHWT better peak signal of noise ratio (PSNR)s can be obtained and that the proposed algorithm fills in better the lack of awareness of the watermark and its strength under different attacks.</span>

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“…The license plate recognition (LPR) technology, which is derived from advanced intelligent transportation systems, plays a vital role in managing traffic, enhancing digital security surveillance, recognizing vehicles, and facilitating parking management in large cities [1][2][3][4][5]. The LPR technology is employed in various countries worldwide, particularly in smart cities, electronic garages, and mainly on roads, to capture data on speeding vehicles or track stolen cars.…”

Section: Introductionmentioning

confidence: 99%

Tesseract OpenCV Versus CNN: A Comparative Study on the Recognition of Unified Modern Iraqi License Plates

Ahmed,

Ali,

Naser

2023

RIA

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Various applications central to societal functioning, such as traffic control and parking management, are fundamentally rooted in License Plate Recognition (LPR). The type of license plate significantly impacts the effectiveness of these processes. This study focuses on the 2022 Unified Modern Iraqi license plates, which pose a unique challenge due to their recent design that incorporates the representation of governorate names with symbols. This new design introduces difficulties in accurately recognizing characters, leading to potential misinformation and unreliable applications. Furthermore, there is a dearth of recognition systems specifically tailored for these newly designed plates. In an attempt to surmount these hurdles, this paper introduces a comparative analysis of two models based on stateof-the-art machine and deep learning methods. The first model employs Tesseract by OpenCV for the recognition of characters on the detected plate, while the second model utilizes a nine-layer Convolutional Neural Network (CNN). The research contributes to the field by collating the plates into a dataset and recognizing them for the first time using these models. The results indicate a significant disparity in the performance of the two models, with the CNN model exhibiting superior accuracy in character recognition, surpassing 95.5%, while the Tesseract OpenCV model achieved a rate of merely 36%. This study underscores the potential of deep learning methods in augmenting license plate recognition systems, especially for novel designs like the 2022 Unified Modern Iraqi license plates.

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Video mosaic watermarking using plasma key

Cited by 7 publications

References 21 publications

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

Performance Evaluation of AES, ECC and Logistic Chaotic Map Algorithms in Image Encryption

The effectiveness of discrete hermite wavelet filters technique in digital image watermarking

Tesseract OpenCV Versus CNN: A Comparative Study on the Recognition of Unified Modern Iraqi License Plates

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