Related papers: Embedding Secret Data in HTML Web Page
Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few…
In this paper, a few novel data hiding techniques are proposed. These techniques are improvements over the classical LSB data hiding technique and the Fibonacci LSB data-hiding technique proposed by Battisti et al. \cite{r1}. The classical…
Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by…
Splitting network computations between the edge device and a server enables low edge-compute inference of neural networks but might expose sensitive information about the test query to the server. To address this problem, existing…
Data compression is very important feature in terms of saving the memory space. In this proposal, an indexed dictionary based compression is used for text data, where the word's reference in dictionary is used for compression. This approach…
Reversible data hiding continues to attract significant attention in recent years. In particular, an increasing number of authors focus on the higher significant bit (HSB) plane of an image which can yield more redundant space. On the other…
Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from unauthorized exploration by adding small perturbations to…
The use of steganography to transmit secret data is becoming increasingly common in security products and malware today. Despite being extremely popular, PDF files are not often the focus of steganography research, as most applications…
Real-time, online-editing web apps provide free and convenient services for collaboratively editing, sharing and storing files. The benefits of these web applications do not come for free: not only do service providers have full access to…
A Covert Channel (CC) exploits legitimate communication mechanisms to stealthily transmit information, often bypassing traditional security controls. Among these, a novel paradigm called History Covert Channels (HCC) leverages past network…
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning…
This paper presents a reversible data hiding in encrypted image (RDHEI), which divides image into non-overlapping blocks. In each block, central pixel of the block is considered as leader pixel and others as follower ones. The prediction…
Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code. As web interfaces are primarily constructed using HTML, we design an in-depth study to…
We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content. To do so, we employ a state-of-the-art graph techniques to first extract the…
Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc. Most of previous work only consider the attribute-level feature embedding, which might perform poorly…
Most popular web browsers include "reader modes" that improve the user experience by removing un-useful page elements. Reader modes reformat the page to hide elements that are not related to the page's main content. Such page elements…
In today's data-driven analytics landscape, deep learning has become a powerful tool, with latent representations, known as embeddings, playing a central role in several applications. In the face analytics domain, such embeddings are…
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these…
Hiding messages for countless security purposes has become a highly fascinating subject nowadays. Encryption facilitates the data hiding. With the express development of technology, people tend to figure out a method capable of hiding a…
Log files record computational events that reflect system state and behavior, making them a primary source of operational insights in modern computer systems. Automated anomaly detection on logs is therefore critical, yet most established…