Geometric Attacks Research Articles

Non-blind Image Watermarking Algorithm based on Non-Separable Haar Wavelet Transform against Image Processing and Geometric Attacks

The protection of digital media become crucial and needs to be developed as technology continues to become more advanced. This is to cater to the problems in copyright protection and data security. This paper presents a new non-blind image watermarking algorithm applying modified non-separable Haar wavelet transform (NSHWT), singular value decomposition (SVD), Arnold’s cat map and Rabin-p cryptosystem. The traditional transform domain watermarking (DWT) is resource-consuming, especially when performed on large image data. In order to improve on this issue, the proposed algorithm applied the modified NSHWT, a much more efficient alternative to the DWT. Another concern of digital watermarking algorithms is it is often publicly known, so encryptions are important to keep the image secure. The embedded watermark image is protected by scrambling the image using Arnold’s cat map, and the scrambling parameters are encrypted with the Rabin-p cryptosystem to keep the algorithm secure. In general, the application of modified NSHWT and SVD makes the watermark highly robust against image processing and geometric processing attacks, and its security is ensured with the protection by Arnold’s cat map and Rabin-p cryptosystem. There are already many algorithms with high imperceptibility and robustness, but in finding the perfect balance, authors often forsake other metrics such as efficiency, security, and flexibility. These are also essential when the algorithm is considered in real applications. Thus, the proposed algorithm is evaluated in terms of imperceptibility, robustness, and efficiency. The algorithm achieved imperceptibility results of 51.5157 average PSNR and 0.9991 average SSIM, and robustness results from 0.9710 to 0.9966 NC values. The algorithm is also around 40% faster to execute with modified NSHWT compared to the traditional DWT. Finally, it has a high embedding capacity of 6 bits per pixel. Overall, results show that the algorithm is highly imperceptible and robust against image processing and geometric attacks, while additionally being secure and flexible.

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today's world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB's high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.

Robust perceptual fingerprint image hashing: a comparative study

This paper presents a robust perceptual hashing scheme for biometric template protection where the input fingerprint image is mapped into a sequence of Boolean values. Our aim is to develop a method that relies on the use of four functions namely SIFT, Harris, DWT and SVD. After extracting the minutiae, the scale-invariant feature transform (SIFT) is applied in order to extract the robust features against geometric attacks. The resulting vector is then filtered using Harris criterion to maintain only the stable key-points. Next, the fingerprint template is produced by image binarisation and decomposed into blocks. The hash code is finally obtained by concatenating the singular values computed on the approximation coefficients of each image block. Similarity between hash codes is evaluated by the normalised Hamming distance (HD). Comparative analysis to three similar methods indicates that the proposed hashing scheme shows better performances in terms of discriminative capability as well as robustness against acceptable image manipulations, such as JPEG compression, gamma correction, speckle noise, Gaussian blur, shearing and slight rotation.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks to high internet speed and high resolution, cheap and easily accessible digital cameras. We generate, transmit and store millions of images every second. Most of these images are insignificant images containing only personal information. However, in many fields such as banking, finance, public institutions, and educational institutions, the images of many valuable objects like ID cards, photographs, credit cards, and transaction receipts are stored and transmitted to the digital environment. These images are very significant and must be secured. A valuable image can be maliciously modified by an attacker. The modification of an image is sometimes imperceptible even by the person who stored the image. In this paper, an active image forgery detection method that encodes and decodes image edge information is proposed. The proposed method is implemented by designing an interface and applied on a test image which is frequently used in the literature. Various tampering attacks are simulated to test the fidelity of the method. The method not only notifies whether the image is forged or not but also marks the tampered region of the image. Also, the proposed method successfully detected tampered regions after geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEG compression.

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key vector is obtained and saved at the same time. Similarly, the same feature extraction method is used to extract the deep features of the medical image to be tested and generate the feature vector. Later, the XOR operation is carried out between the feature vector and the logical key vector, and the encrypted watermark is extracted and decrypted to get the restored watermark; the normalized correlation coefficient (NC) of the original watermark and the restored watermark is calculated to determine the ownership and watermark information of the medical image to be tested. After calculation, most of the NC values are greater than 0.50. The experimental results demonstrate the algorithm’s robustness, invisibility, and security, as well as its ability to accurately extract watermark information. The algorithm also shows good resistance to conventional attacks and geometric attacks.

A Multi-Watermarking Algorithm for Medical Images Using Inception V3燼nd燚CT

Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes a novel watermarking algorithm using the convolutional neural networks (CNN) Inception V3 and the discrete cosine transform (DCT) to address above mentioned problems. First, the medical image is input into the Inception V3 network, which has been structured by adjusting parameters, such as the size of the convolution kernels and the typical architecture of the convolution modules. Second, the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image. At last, the watermarks are encrypted using the logistic map system and hash function, and the keys are stored by a third party. The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark. In the experimental section, multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms. Our NC values for all the images are more than 90% accurate which shows the robustness of the algorithm. Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset, so that the pre-trained network can further learn the deep convolutional features in the medical dataset, and then the trained network is used to extract the stable feature vectors of medical images. Then, a two-dimensional Henon chaos encryption technique, which is more sensitive to initial values, is used to encrypt multiple different types of watermarked private information. Finally, the feature vector of the image is logically operated with the encrypted multiple watermark information, and the obtained key is stored in a third party, thus achieving zero watermark embedding and blind extraction. The experimental results confirm the robustness of the algorithm from the perspective of multiple types of watermarks, while also demonstrating the successful embedding of multiple watermarks for medical images, and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

A Hybrid SVD-Based Image Watermarking Scheme Utilizing Both U and V Orthogonal Vectors for Robustness and Imperceptibility

SVD-based watermarking algorithm is one of the most preferable algorithms for copyright protection due to its singular values (SVs) that have outstanding stability and represents intrinsic algebraic image properties. Hence, there is a good trade-off between robustness and imperceptibility. However, most SVD-based algorithms have been tested against conventional attacks, such as image manipulations, that do not fully exploit adversary’s knowledge. These algorithms are vulnerable to false-positive problem, where an adversary’s watermark can be detected in the watermarked image although it was never inserted. The underlying problem is due to the strong influence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> orthogonal vectors of SVD on an image. In order to solve false-positive problem, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$UV$ </tex-math></inline-formula> can be used to embed the watermark together with SVs. However, this solution is not ideal as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> hold important structural information of an image and is hypersensitivity to even a little change in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$UV$ </tex-math></inline-formula> vectors. Therefore, this research has the objectives to analyse the robustness of existing SVD-based watermarking algorithms that are using orthogonal vectors and then propose a new robust algorithm that is able to solve false-positive problem and sensitivity issued caused by scaling factor. Hence, a new transform domain image watermarking scheme that utilized both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> orthogonal vectors is proposed with the usage of Human Visual System (HVS), Discrete Wavelet Transform (DWT) and SVD. Experimental results showed that the proposed scheme is more robust against majority types of image processing and geometrical attacks compared to existing schemes while achieving good quality watermarked image level. The significance of new algorithm comes at the right time during the Covid-19 epidemic as organizations involving in business and financial services can be assured of the integrity of its downloadable/streamable/shareable digital files, which are copyrighted through the unique SVD and robustness features of the algorithm ensuring piracy prevention of their content.

CADEN: cellular automata and DNA based secure framework for privacy preserving in IoT based healthcare.

In the present E-healthcare industry, data breaches result in substantial economic losses due to cyber-attacks and hence create a trust deficit between the industry and users. The healthcare industry has rapidly adopted IoT frameworks but the trust deficit and privacy concerns limit its utilization among the masses. Along with privacy protection, content authentication is an important requirement in a number of critical applications and fragile watermarking provides an effective solution. However, existing fragile watermarking techniques lack the accuracy of tamper detection and hence are not reliable enough in terms of security and privacy of the data. This paper presents a novel low-complexity block-based fragile watermarking technique with high security against cyber-security attacks. This is achieved by embedding a fragile watermark in the host image using pixel domain blocking approach. The security of embedded watermark has been taken care of by using Cellular Automata and DNA based ENcryption (CADEN) framework to scramble the watermark bits using various secret keys. Experimental investigations show that besides being highly secure, the proposed technique is fragile to various signal processing and geometric attacks. The comparative analysis shows that the proposed scheme, despite having lower complexity, offers better efficiency in terms of imperceptibility, tamper detection and localization compared to other state-of-the-art techniques. Besides, the fragile watermark embedding makes the system capable to preserve the secret information in case of an attack with an average BER of 40%.

Robust zero-watermarking scheme based on a depthwise overparameterized VGG network in healthcare information security

As healthcare information technology has rapidly evolved, securely storing and transmitting medical data online and successfully protecting patient privacy are currently the research focus in the healthcare information field. To better protect the security of medical data, this paper introduces deep neural network and convolutional block attention module (CBAM) into the study of watermarking techniques and proposes a medical image zero-watermarking scheme based on depthwise overparameterized VGG (DO-VGG). First, we extract the high-dimensional abstract feature information of medical images using the pretrained DO-VGG model. Then, the construction of the zero-watermarking scheme utilizes the mean-perceptual hashing algorithm, which can efficiently resist both common and geometric attacks. Meanwhile, using the improved logistic mapping to encrypt the watermarking image effectively improves the security of the scheme. Experimental results indicate that all NC values of the proposed scheme are maintained above 0.8 under various degrees of attacks, which has good robustness and invisibility. The proposed scheme can satisfy the special requirements of medical image integrity and effectively protect the private information of medical images.

Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.

A robust blind watermarking scheme based on lifting wavelet transform and hessenberg decomposition

In this paper, a novel robust blind grayscale image digital watermarking scheme is introduced based on lifting wavelet transform (LWT) in combination with discrete cosine transform (DCT), Hessenberg decomposition, and entropy analysis for copyright protection of multimedia information. At first, the two levels of LWT are applied to the host grayscale image to improve the imperceptibility of the watermarking scheme and then the high-frequency sub-band of the 2-level of LWT is decomposed by DCT. Next, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Hessenberg decomposition performs on each selected block, whereas the first row, first column element of the upper Hessenberg matrix is utilized to hide the watermark. To evaluate the imperceptibility and robustness of the proposed digital watermarking scheme, the peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NC) are utilized to measure the quality and the ability of the proposed watermarking scheme to robust against signal processing operations and geometric attacks. Experimental and analysis results have demonstrated that the proposed scheme is achieved a very good tradeoff between imperceptibility and robustness. The comparison with other scheme have shown that the proposed digital watermarking schemes have a superior performance in terms of imperceptibility and robustness than other.

Robust watermarking algorithm for medical images based on accelerated‐KAZE discrete cosine transform

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated-KAZE discrete cosine transform (AKAZE-DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE-DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero-watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.

A robust encryption watermarking algorithm for medical images based on ridgelet-DCT and THM double chaos

With the help of big data, cloud computing, artificial intelligence and other technologies, the informatization and intelligence of the wisdom medical have been gradually realized. However, with the transmission and storage of massive amounts of medical images in the cloud, information security issues have become increasingly prominent. The privacy of patients is at risk of disclosure, theft and tampering, which has become an important challenge restricting the development of wisdom medical. How to protect the personal information of patients in the cloud environment has become an urgent problem to be solved. Medical image watermarking technology is an effective method to solve this problem. Combining the characteristics of Tent chaos and Henon chaos, this paper designed a Tent-Henon-Map double chaos watermarking encryption method and designed a medical image encryption watermarking algorithm based on ridgelet-DCT transform. The watermark images were encrypted by the Tent-Henon-Map double chaos which had the characteristics of sensitive initial values and large key space. Then, the feature vectors of the medical images were extracted through ridgelet-DCT transform. On the basis of ordinary watermarking technology, combined with zero watermarking, third-party concepts, and cryptographic technology, watermarking had a good ability to resist image processing attacks. The experimental results showed that the key space of the algorithm was {10}^{116}, which had better encryption and hard to crack. The time of watermark embedding and extraction were only 0.336 s and 0.439 s, with lower computational cost. And under high-strength conventional attacks and geometric attacks, the NC values of the algorithm were all greater than 0.55, which could effectively extract watermark information. It shown that the algorithm proposed had good robustness against conventional and geometric attacks It shown that the algorithm proposed had good robustness against conventional and geometric attacks, while taking into account the security.

A novel secured Euclidean space points algorithm for blind spatial image watermarking

Digital raw images obtained from the data set of various organizations require authentication, copyright protection, and security with simple processing. New Euclidean space point’s algorithm is proposed to authenticate the images by embedding binary logos in the digital images in the spatial domain. Diffie–Hellman key exchange protocol is implemented along with the Euclidean space axioms to maintain security for the proposed work. The proposed watermarking methodology is tested on the standard set of raw grayscale and RGB color images. The watermarked images are sent in the email, WhatsApp, and Facebook and analyzed. Standard watermarking attacks are also applied to the watermarked images and analyzed. The finding shows that there are no image distortions in the communication medium of email and WhatsApp. But in the Facebook platform, raw images experience compression and observed exponential noise on the digital images. The authentication and copyright protection are tested from the processed Facebook images. It is found that the embedded logo could be recovered and seen with added noise distortions. So the proposed method offers authentication and security with compression attacks. Similarly, it is found that the proposed methodology is robust to JPEG compression, image tampering attacks like collage attack, image cropping, rotation, salt-and-pepper noise, sharpening filter, semi-robust to Gaussian filtering, and image resizing, and fragile to other geometrical attacks. The receiver operating characteristics (ROC) curve is drawn and found that the area under the curve is approximately equal to unity and restoration accuracy of [67 to 100]% for various attacks.

Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.

A robust and secure watermarking algorithm based on DWT and SVD in the fractional order fourier transform domain

The present paper aims to develop and validate a robust and secure watermarking algorithm. Firstly, the algorithm transforms the cover image and the watermark image separately by FRFT to obtain the amplitudes of both the cover image and watermark image, and then the two-level DWT transformation only carries out for the amplitude of the cover image. Secondly, the algorithm applies the SVD to the low-frequency sub-band of the second level DWT of the cover image and the amplitude of the watermark image. Thirdly, a new matrix is constructed based on the singular values from both the cover and watermark images to embed watermark information. Lastly, the algorithm takes the preliminary numerical calculation to determine an appropriate transformation order of FRFT for the cover image and to provide an outstanding balance between the imperceptibility and robustness in the proposed watermarking scheme. Moreover, it guarantees the security of watermarking scheme by the transformation order of FRFT. Experimental results demonstrate that the proposed watermarking scheme provides better performance in imperceptibility and resistance against traditional signal processing and geometric attacks, especially in image rotation, image cropping, average filtering, median filtering, and Gaussian filtering in comparison with the existing methods in the literature.

An invisible and robust watermarking scheme using convolutional neural networks

Although zero-watermarking schemes based on image features demonstrate perfect imperceptibility, they are exposed to the weak robust problem. This is because the zero-watermark information construction and the watermark information detection depend on XOR logic operation. In this study, a watermarking scheme that constructs a zero-watermark image by superimposing the color style of the host image on the content of the watermark logo using convolutional neural networks is proposed. A stylized image is generated by iterating between the style of the host image and the content of a watermark logo added with a timestamp. The stylized image is then encrypted via Arnold transform and is registered in the intellectual property rights as the zero-watermark image. The image copyright is verified using the convolutional neural network whose loss function is defined as the difference between the original watermark logo and its output image. Its training dataset consists of many image pairs, each of which is composed of the host image under an attack and the decrypted zero-watermark image. Experimental results show that the proposed zero-watermarking scheme is highly robust to both common image processing and geometric attacks, and its performance far surpasses those of the existing zero-watermarking methods and non-zero-watermarking methods.

Image Recognition and Encryption Algorithm Based on Artificial Neural Network and Multidimensional Chaotic Sequence.

With the continuous development of Internet technology and technological innovation, image recognition technologies such as face unlocking and face brushing payment have gradually entered daily life. However, it can not be ignored that these technologies not only bring us great convenience but also face great risks. The biological characteristics of a face image are unique, and it will be difficult to modify once it is leaked. If the image information stored in the cloud is leaked because it cannot be properly kept, users have no privacy. The encryption and recognition of face image can effectively solve this problem. Aiming at this, high-dimensional chaos Henon Map and one-dimensional chaos Logistic Map are used to generate a key to complete the encryption of the image in the transformation domain, and the capacity and complexity of the key are further enhanced. Then, combined with BP neural network to achieve face image recognition. Finally, the robustness of the proposed algorithm is verified and analyzed by conventional attacks, geometric attacks, and occlusion attacks.

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map.

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.