An Automatic Identification Algorithm for Encrypted Anti-counterfeiting Tag Based on DWT-DCT and Chen’s Chaos

Dai, Qianning; Li, Jingbing; Bhatti, Uzair Aslam; Cheng, Jieren; Bai, Xiaobo

doi:10.1007/978-3-030-24271-8_53

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To strengthen the algorithm's resilience to attacks, we execute another DCT transform on the data extracted from the CNN [34]. This concentrates the energy in the image signal, making it easier to retrieve image features.…”

Section: Discrete Cosine Transformmentioning

confidence: 99%

“…Different studies used the logistic map for encryption of information, Szegedy et al scrambled the row and column for each pixel in the input image using two 1-D discrete for watermark encryption, similarly [33]. Dai et al also used logistic mapping for generating encrypted watermark sequences for improving the security of medical images [34]. Therefore, Logistic mapping is one of the key factors in securing the image with better security.…”

Section: Logistic Mapmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discrete Cosine Transformmentioning

confidence: 99%

Section: Logistic Mapmentioning

confidence: 99%

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

Yang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…This technology is found either in labels or documents of transport and certifications of goods. Due to their encrypted nature, specialized equipment is required in order to decode them [7].…”

Section: Covert Technologiesmentioning

confidence: 99%

A Review and Classification Framework of Traceability Approaches for Identifying Product Supply Chain Counterfeiting

et al. 2022

View full text Add to dashboard Cite

Counterfeiting is found today in many industries and in various forms with severe consequences for supply chain operations. Products counterfeiting can be detected in consumer goods such as clothing, food and beverages, accessories, pharmaceuticals, electronics, and luxury goods. The continuous violations in the supply chain have led to the need for mobilization of all involved stakeholders to overcome counterfeiting challenges. Effective traceability seems to be the only way to combat this phenomenon, ensuring safe and sustainable supply chain operations. This paper presents a structured literature review on traceability approaches for combatting the product supply chain counterfeiting phenomenon that led to forming a structured classification framework. The performed analysis aims to identify trends and good practices and can be used as a guideline for real-life projects against supply chain counterfeiting. The results show that traditional traceability methods are not effective as they can be easily falsified using today’s technological advancements. However, these same advancements also present valuable technologies such as blockchain and the internet of things to ensure safe and sustainable supply chain operations.

show abstract

Advance Watermarking Algorithm Using SURF with DWT and DCT for CT Images

Nawaz

Bhatti

et al. 2020

Innovation in Medicine and Healthcare

View full text Add to dashboard Cite

An Automatic Identification Algorithm for Encrypted Anti-counterfeiting Tag Based on DWT-DCT and Chen’s Chaos

Cited by 7 publications

References 9 publications

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

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

A Review and Classification Framework of Traceability Approaches for Identifying Product Supply Chain Counterfeiting

Advance Watermarking Algorithm Using SURF with DWT and DCT for CT Images

Contact Info

Product

Resources

About