Perspective Chapter: Text Watermark Analysis - Concept, Technique, and Applications

Watermarking is a modern technology in which identifying information is embedded in a data carrier. It is not easy to notice without affecting data usage. A text watermark is an approach to inserting a watermark into text documents. This is an extremely complex undertaking, especially given the scarcity of research in this area. This process has proven to be very complex, especially since there has only been a limited amount of research done in this field. Conducting an in-depth analysis, analysis, and implementation of the evaluation, is essential for its success. The overall aim of this chapter is to develop an understanding of the theory, methods, and applications of text watermarking, with a focus on procedures for defining, embedding, and extracting watermarks, as well as requirements, approaches, and linguistic implications. Detailed examination of the new classification of text watermarks is provided in this chapter as are the integration process and related issues of attacks and language applicability. Research challenges in open and forward-looking research are also explored, with emphasis on information integrity, information accessibility, originality preservation, information security, and sensitive data protection. The topics include sensing, document conversion, cryptographic applications, and language flexibility

Secure Detection of Image Manipulation by means of Random Feature Selection

We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector. Specifically, we assume that the attacker knows the architecture of the detector, the training data and the class of features V the detector can rely on. In order to get an advantage in his race of arms with the attacker, the analyst designs the detector by relying on a subset of features chosen at random in V. Given its ignorance about the exact feature set, the adversary attacks a version of the detector based on the entire feature set. In this way, the effectiveness of the attack diminishes since there is no guarantee that attacking a detector working in the full feature space will result in a successful attack against the reduced-feature detector. We theoretically prove that, thanks to random feature selection, the security of the detector increases significantly at the expense of a negligible loss of performance in the absence of attacks. We also provide an experimental validation of the proposed procedure by focusing on the detection of two specific kinds of image manipulations, namely adaptive histogram equalization and median filtering. The experiments confirm the gain in security at the expense of a negligible loss of performance in the absence of attacks

Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient

Tamper-proofing of Electronic and Printed Text Documents via Robust Hashing and Data-Hiding

In this paper, we deal with the problem of authentication and tamper-proofing of text documents that can be distributed in electronic or printed forms. We advocate the combination of robust text hashing and text datahiding technologies as an efficient solution to this problem. First, we consider the problem of text data-hiding in the scope of the Gel’fand-Pinsker data-hiding framework. For illustration, two modern text data-hiding methods, namely color index modulation (CIM) and location index modulation (LIM), are explained. Second, we study two approaches to robust text hashing that are well suited for the considered problem. In particular, both approaches are compatible with CIM and LIM. The first approach makes use of optical character recognition (OCR) and a classical cryptographic message authentication code (MAC). The second approach is new and can be used in some scenarios where OCR does not produce consistent results. The experimental work compares both approaches and shows their robustness against typical intentional/unintentional document distortions including electronic format conversion, printing, scanning, photocopying, and faxing. 1