The current study presents a new methodology for zero watermarking that relies on the discrete wavelet transform (DWT) and an improved MobileNetV2 convolutional neural network, along with the discrete cosine transform (DCT). This model is intended to overcome the issue of algorithm robustness in encrypted medical image watermarking. The proposed technique targets medical images inside the area of encryption and offers a unique watermarking approach to overcome the issues above. The coefficients acquired from the fully linked network layer are converted using DWT and DCT to create the medical image's feature vector. Second, MobileNetV2 is initially fed the medical image; this network has been prepared by adjusting parameters such as the convolution kernels' size and the convolution modules' typical architecture. Finally, the encryption of watermarks is achieved through the utilization of the logistic map system and hash function, whereby an independent party securely stores the requisite keys. The integration of a zero-watermark involves the execution of logical operations on both the encrypted watermarks and the attributes of the source image. The results of the experiment indicate that the algorithm can effectively differentiate encrypted medical images and recover the initial data from encrypted watermarked material despite conventional and geometric attacks. Compared to alternative algorithms, their superior resilience and invisibility are noteworthy. 

Hybrid watermarking algorithm for medical images based on digital transformation and MobileNetV2

Improving fine-tuning of self-supervised models with Contrastive Initialization

In remote sensing image classification, active learning aims to obtain an excellent classification model by selecting informative or representative training samples. However, due to the complexity of remote sensing images, the same class of ground objects usually have different spectral representations. The existing active learning methods may not take into account diverse representations of the same targets, which leads to a possible lack of intra-class diversity in the collected samples. To alleviate this problem, we propose an active learning method based on similarity level histogram (SLH) and adaptive-scale sampling to improve very high resolution remote sensing image classification. Specifically, we construct a SLH for each class of ground objects to effectively consider the intra-class diversity of the same target. To avoid the problem of sample imbalance caused by over-sampling or under-sampling, we design an adaptive-scale sampling strategy. Then, we utilize active learning to mine representative samples from each SLH warehouse according to adaptive-scale sampling strategies until the iteration condition is satisfied. Experiments show that the proposed algorithm can achieve better classification performance with limited training samples and is competitive with other methods based on four sets of publicly available data. 

Active learning based on similarity level histogram and adaptive-scale sampling for very high resolution image classification

In this paper, we propose a Swin Transformer-based perceptual image hashing scheme to resist the impact of screen-shooting distortion on content authentication. The system applies a Swin Transformer-based U-shaped architecture to form a feature extractor, cooperates through encoder-decoder for feature extraction and compression, and employs a hash generator consisting of several fully connected layers to generate the final hash sequence. In addition, the scheme adds a distortion simulator to the training process, which uses image processing operations to simulate distortion on the original image to assist the feature extractor and hash generator in learning to resist screen-shooting distortion. Our experiments show that the proposed hashing is robust to single and hybrid content-preserving operations in screen-shooting attack. In addition, we also demonstrate that our hashing scheme outperforms some state-of-the-art schemes in terms of robustness and discrimination. 

Robust Perceptual Image Hashing for Screen-Shooting Attack

The era of digital space has increased the chances of illicit use of digital content exponentially, thus posing innumerable threats to legit content. This issue is tackled efficiently using the perceptual image hashing systems. This study provides an overview of the previous and current literature on the variety of techniques that are employed for the creation of perceptual hash. It also categorizes the kind of manipulation that is investigated in the literature. Moreover, the framework, different evaluation criteria, and performance metrics are also discussed. The paper presents future recommendations and drawn conclusions from the surveyed techniques. 

Various Approaches to perceptual image hashing systems-A Survey*

Robust image hashing for content identification through contrastive self-supervised learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Mikael Hård

Author Tools

Chat about Author

Papers

Robust image hashing for content identification through contrastive self-supervised learning