Visual and Textual Analysis for Image Trustworthiness Assessment within Online News

The majority of news published online presents one or more images or videos, which make the news more easily consumed and therefore more attractive to huge audiences. As a consequence, news with catchy multimedia content can be spread and get viral extremely quickly. Unfortunately, the availability and sophistication of photo editing software are erasing the line between pristine and manipulated content. Given that images have the power of bias and influence the opinion and behavior of readers, the need of automatic techniques to assess the authenticity of images is straightforward. This paper aims at detecting images published within online news that have either been maliciously modified or that do not represent accurately the event the news is mentioning. The proposed approach composes image forensic algorithms for detecting image tampering, and textual analysis as a verifier of images that are misaligned to textual content. Furthermore, textual analysis can be considered as a complementary source of information supporting image forensics techniques when they falsely detect or falsely ignore image tampering due to heavy image postprocessing. The devised method is tested on three datasets. The performance on the first two shows interesting results, with F1-score generally higher than 75%. The third dataset has an exploratory intent; in fact, although showing that the methodology is not ready for completely unsupervised scenarios, it is possible to investigate possible problems and controversial cases that might arise in real-world scenarios

Joint watermarking and encryption of color images in the Fibonacci-Haar domain

A novel method for watermarking and ciphering color images, based on the joint use of a key-dependent wavelet transform with a secure cryptographic scheme, is presented. The system allows to watermark encrypted data without requiring the knowledge of the original data and also to cipher watermarked data without damaging the embedded signal. Since different areas of the proposed transform domain are used for encryption and watermarking, the extraction of the hidden information can be performed without deciphering the cover data and it is also possible to decipher watermarked data without removing the watermark. Experimental results show the effectiveness of the proposed scheme

More Real than Real: A Study on Human Visual Perception of Synthetic Faces

Deep fakes became extremely popular in the last years, also thanks to their increasing realism. Therefore, there is the need to measures human's ability to distinguish between real and synthetic face images when confronted with cutting-edge creation technologies. We describe the design and results of a perceptual experiment we have conducted, where a wide and diverse group of volunteers has been exposed to synthetic face images produced by state-of-the-art Generative Adversarial Networks (namely, PG-GAN, StyleGAN, StyleGAN2). The experiment outcomes reveal how strongly we should call into question our human ability to discriminate real faces from synthetic ones generated through modern AI

Overview of ImageCLEF lifelog 2017: lifelog retrieval and summarization

Despite the increasing number of successful related work- shops and panels, lifelogging has rarely been the subject of a rigorous comparative benchmarking exercise. Following the success of the new lifelog evaluation task at NTCIR-12, the first ImageCLEF 2017 LifeLog task aims to bring the attention of lifelogging to a wide audience and to promote research into some of the key challenges of the coming years. The ImageCLEF 2017 LifeLog task aims to be a comparative evaluation framework for information access and retrieval systems operating over personal lifelog data. Two subtasks were available to participants; all tasks use a single mixed modality data source from three lifeloggers for a period of about one month each. The data contains a large collection of wearable camera images, an XML description of the semantic locations, as well as the physical activities of the lifeloggers. Additional visual concept information was also provided by exploiting the Caffe CNN-based visual concept detector. For the two sub-tasks, 51 topics were chosen based on the real interests of the lifeloggers. In this first year three groups participated in the task, submitting 19 runs across all subtasks, and all participants also provided working notes papers. In general, the groups performance is very good across the tasks, and there are interesting insights into these very relevant challenges

Organizer team at ImageCLEFlifelog 2017: baseline approaches for lifelog retrieval and summarization

This paper describes the participation of Organizer Team in the ImageCLEFlifelog 2017 Retrieval and Summarization subtasks. In this paper, we propose some baseline approaches, using only the provided information, which require different involvement levels from the users. With these baselines we target at providing references for other approaches that aim to solve the problems of lifelog retrieval and summarization