Digital images can be copied without authorization and have to be protected. Two schemes for watermarking images in PDF document were considered. Both schemes include a converter to extract images from PDF pages and return the protected images back. Frequency and spatial domain embedding were used for hiding a message presented by a binary pattern. We considered visible and invisible watermarking and found that spatial domain LSB technique can be more preferable than frequency embedding using DWT.
The advancement in digital technologies have made it possible to produce perfect copies of digital content. In this environment, malicious users reproduce the digital content and share it without compensation to the content owner. Content owners are concerned about the potential loss of revenue and reputation from piracy, especially when the content is available over the Internet. Digital watermarking has emerged as a deterrent measure towards such malicious activities. Several methods have been proposed for copyright protection and fingerprinting of digital images. However, these methods are not applicable to text documents as these documents lack rich texture information which is abundantly available in digital images. In this paper, a framework (mPDF) is proposed which facilitates the usage of digital image watermarking algorithms on text documents. The proposed method divides a text document into texture and non-texture blocks using an energy-based approach. After classification, a watermark is embedded inside the texture blocks in a content adaptive manner. The proposed method is integrated with five known image watermarking methods and its performance is studied in terms of quality and robustness. Experiments are conducted on documents in 11 different languages. Experimental results clearly show that the proposed method facilitates the usage of image watermarking algorithms on text documents and is robust against attacks such as print & scan, print screen, and skew. Also, the proposed method overcomes the drawbacks of existing text watermarking methods such as manual inspection and language dependency.
Steganalysis means analysis of stego images. Like cryptanalysis, steganalysis is used to detect messages often encrypted using secret key from stego images produced by steganography techniques. Recently lots of new and improved steganography techniques are developed and proposed by researchers which require robust steganalysis techniques to detect the stego images having minimum false alarm rate. This paper discusses about the different Steganalysis techniques and help to understand how, where and when this techniques can be used based on different situations.
Facebook is the online social networks (OSNs) platform with the largest number of users in the world today, information protection based on Facebook social network platform have important practical significance. Since the information users share on social networks is often based on images, this paper proposes a more secure image encryption algorithm based on Facebook social network platform to ensure the loss of information as much as possible. When the sender encrypts the image for uploading, it can first resist the third partys attack on the encrypted image and prevent the image data from leaking, simultaneously processed by some unknown processing such as compression and filtering of the image on the Facebook platform, the receiver can still decrypt the corresponding image data.
The unprecedented ease and ability to manipulate video content has led to a rapid spread of manipulated media. The availability of video editing tools greatly increased in recent years, allowing one to easily generate photo-realistic alterations. Such manipulations can leave traces in the metadata embedded in video files. This metadata information can be used to determine video manipulations, brand of video recording device, the type of video editing tool, and other important evidence. In this paper, we focus on the metadata contained in the popular MP4 video wrapper/container. We describe our method for metadata extractor that uses the MP4s tree structure. Our approach for analyzing the video metadata produces a more compact representation. We will describe how we construct features from the metadata and then use dimensionality reduction and nearest neighbor classification for forensic analysis of a video file. Our approach allows one to visually inspect the distribution of metadata features and make decisions. The experimental results confirm that the performance of our approach surpasses other methods.
Emerging applications in multiview streaming look for providing interactive navigation services to video players. The user can ask for information from any viewpoint with a minimum transmission delay. The purpose is to provide user with as much information as possible with least number of redundancies. The recent concept of navigation segment representation consists of regrouping a given number of viewpoints in one signal and transmitting them to the users according to their navigation path. The question of the best description strategy of these navigation segments is however still open. In this paper, we propose to represent and code navigation segments by a method that extends the recent layered depth image (LDI) format. It consists of describing the scene from a viewpoint with multiple images organized in layers corresponding to the different levels of occluded objects. The notion of extended LDI comes from the fact that the size of this image is adapted to take into account the sides of the scene also, in contrary to classical LDI. The obtained results show a significant rate-distortion gain compared to classical multiview compression approaches in navigation scenario.