Showing papers by "Duc-Tien Dang-Nguyen published in 2015"

RAISE: a raw images dataset for digital image forensics

Abstract: Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

Verifying Multimedia Use at MediaEval 2015

Abstract: This paper provides an overview of the Verifying Multimedia Use task that takes places as part of the 2015 MediaEval Benchmark. The task deals with the automatic detection of manipulation and misuse of Web multimedia content. Its aim is to lay the basis for a future generation of tools that could assist media professionals in the process of verication. Examples of manipulation include maliciously tampering with images and videos, e.g., splicing, removal/addition of elements, while other kinds of misuse include the reposting of previously captured multimedia content in a dierent context (e.g., a new event) claiming that it was captured there. For the 2015 edition of the task, we have generated and made available a large corpus of real-world cases of images that were distributed through tweets, along with manually assigned labels regarding their use, i.e. misleading (fake) versus appropriate (real).

A hybrid approach for retrieving diverse social images of landmarks

Abstract: In this paper, we present a novel method that can produce a visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. The main idea of this method is to filter out non-relevant images at a first stage and then cluster the images according to textual descriptors first, and then to visual descriptors. The extraction of images from different clusters according to a measure of user's credibility, allows obtaining a reliable set of diverse and relevant images. Experimental results performed on the MediaEval 2014 “Retrieving Diverse Social Images” dataset show that the proposed approach can achieve very good performance outperforming state-of-art techniques.

3D-Model-Based Video Analysis for Computer Generated Faces Identification

Abstract: Modern computer graphics technologies brought realism in computer-generated characters, making them achieve truly natural appearance. Besides traditional virtual reality applications such as avatars, games, or cinema, these synthetic characters may be used to generate realistic fakes, which may lead to improper use of the technology. This fact raises the demand for advanced tools able to discriminate real and artificial human faces in digital media. In this paper, we propose a method to distinguish between computer generated and natural faces by modeling and evaluating their dynamic behavior. Because of a 3D-model-based video analysis, the proposed technique allows identifying synthetic characters by detecting their more limited variability over time. Experimental results demonstrate the effectiveness of the proposed approach also on very challenging and realistic video sequences.

The CERTH-UNITN Participation @ Verifying Multimedia Use 2015

Abstract: We propose an approach that predicts whether a tweet, which is accompanied by multimedia content (image/video), is trustworthy or deceptive. We test dierent combinations of quality and trust-oriented features (tweet-based, userbased and forensics) in tandem with a standard classication and an agreement-retraining technique, with the goal of predicting the most likely label (fake or real) for each tweet. The experiments carried out on the Verifying Multimedia Use dataset show that the best performance is achieved when using all available features in combination with the agreement-retraining method.

Multimodal-based Diversified Summarization in Social Image Retrieval

Abstract: In this paper, we describe our approach and its results for the MediaEval 2015 Retrieving Diverse Social Images task. The main strength of the proposed approach is its exibility that permits to lter out irrelevant images, and to obtain a reliable set of diverse and relevant images. This is done by rst clustering similar images according to their textual descriptions and their visual content, and then extracting images from dierent clusters according to a measure of user’s credibility. Experimental results shown that it is stable and has little uctuation in both single-concept and multi-concept queries.