Benford's law: what does it say on adversarial images?

Convolutional neural networks (CNNs) are fragile to small perturbations in the input images. These networks are thus prone to malicious attacks that perturb the inputs to force a misclassification. Such slightly manipulated images aimed at deceiving the classifier are known as adversarial images. In this work, we investigate statistical differences between natural images and adversarial ones. More precisely, we show that employing a proper image transformation and for a class of adversarial attacks, the distribution of the leading digit of the pixels in adversarial images deviates from Benford's law. The stronger the attack, the more distant the resulting distribution is from Benford's law. Our analysis provides a detailed investigation of this new approach that can serve as a basis for alternative adversarial example detection methods that do not need to modify the original CNN classifier neither work on the raw high-dimensional pixels as features to defend against attacks

On the use of Benford's law to detect GAN-generated images

The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose

Stay True to the Sound of History: Philology, Phylogenetics and Information Engineering in Musicology

This work investigates computational musicology for the study of tape music works tackling the problems concerning stemmatics. These philological problems have been analyzed with an innovative approach considering the peculiarities of audio tape recordings. The paper presents a phylogenetic reconstruction strategy that relies on digitizing the analyzed tapes and then converting each audio track into a two-dimensional spectrogram. This conversion allows adopting a set of computer vision tools to align and equalize different tracks in order to infer the most likely transformation that converts one track into another. In the presented approach, the main editing techniques, intentional and unintentional alterations and different configurations of a tape recorded are estimated in phylogeny analysis. The proposed solution presents a satisfying robustness to the adoption of the wrong reading setup together with a good reconstruction accuracy of the phylogenetic tree. The reconstructed dependencies proved to be correct or plausible in 90% of the experimental cases

Video phylogeny tree reconstruction using aging measures

The increasing diffusion of user-friendly editing software and online media sharing platforms has brought forth a growing on-line availability of near-duplicate (NO) videos. The need of authenticating these contents and tracing back their history has led to the investigation of forensic algorithms for the reconstruction of the video phylogeny tree (VPT), i.e., an acyclic directed graph summarizing video genealogical relationships. Unfortunately, state-of-the-art solutions for VPT reconstruction sufTer from strong computational requirements. In this paper, we propose a processing age measure based on video OCT coefficients and motion vectors statistics, which enables to provide preliminary information about possible video parent-child relationship. The use of processing age allows a forensic analyst to blindly select a smaller amount of significant video pairs to be compared for VPT reconstruction. This solution grants computational complexity reduction to the overall VPT reconstruction pipeline

Reconstrução de filogenias para imagens e vídeos

Orientadores: Anderson de Rezende Rocha, Zanoni DiasTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Com o advento das redes sociais, documentos digitais (e.g., imagens e vídeos) se tornaram poderosas ferramentas de comunicação. Dada esta nova realidade, é comum esses documentos serem publicados, compartilhados, modificados e republicados por vários usuários em diferentes canais da Web. Além disso, com a popularização de programas de edição de imagens e vídeos, muitas vezes não somente cópias exatas de documentos estão disponíveis, mas, também, versões modificadas das fontes originais (duplicatas próximas). Entretanto, o compartilhamento de documentos facilita a disseminação de conteúdo abusivo (e.g., pornografia infantil), que não respeitam direitos autorais e, em alguns casos, conteúdo difamatório, afetando negativamente a imagem pública de pessoas ou corporações (e.g., imagens difamatórias de políticos ou celebridades, pessoas em situações constrangedoras, etc.). Muitos pesquisadores têm desenvolvido, com sucesso, abordagens para detecção de duplicatas de documentos com o intuito de identificar cópias semelhantes de um dado documento multimídia (e.g., imagem, vídeo, etc.) publicado na Internet. Entretanto, somente recentemente têm se desenvolvido as primeiras pesquisas para ir além da detecção de duplicatas e encontrar a estrutura de evolução de um conjunto de documentos relacionados e modificados ao longo do tempo. Para isso, é necessário o desenvolvimento de abordagens que calculem a dissimilaridade entre duplicatas e as separem corretamente em estruturas que representem a relação entre elas de forma automática. Este problema é denominado na literatura como Reconstrução de Filogenia de Documentos Multimídia. Pesquisas na área de filogenia de documentos multimídia são importantes para auxiliar na resolução de problemas como, por exemplo, análise forense, recuperação de imagens por conteúdo e rastreamento de conteúdo ilegal. Nesta tese de doutorado, apresentamos abordagens desenvolvidas para solucionar o problema de filogenias para imagens e vídeos digitais. Considerando imagens, propomos novas abordagens para tratar o problema de filogenia considerando dois pontos principais: (i) a reconstrução de florestas, importante em cenários onde se tem um conjunto de imagens semanticamente semelhantes, mas geradas por fontes ou em momentos diferentes no tempo; e (ii) novas medidas para o cálculo de dissimilaridade entre as duplicatas, uma vez que esse cálculo afeta diretamente a qualidade de reconstrução da filogenia. Os resultados obtidos com as soluções para filogenia de imagens apresentadas neste trabalho confirmam a efetividade das abordagens propostas, identificando corretamente as raízes das florestas (imagens originais de uma sequencia de evolução) com até 95% de acurácia. Para filogenia de vídeos, propomos novas abordagens que realizam alinhamento temporal nos vídeos antes de se calcular a dissimilaridade, uma vez que, em cenários reais, os vídeos podem estar desalinhados temporalmente, terem sofrido recorte temporal ou serem comprimidos, por exemplo. Nesse contexto, nossas abordagens conseguem identificar a raiz das árvores com acurácia de até 87%Abstract: Digital documents (e.g., images and videos) have become powerful tools of communication with the advent of social networks. Within this new reality, it is very common these documents to be published, shared, modified and often republished by multiple users on different web channels. Additionally, with the popularization of image editing software and online editor tools, in most of the cases, not only their exact duplicates will be available, but also manipulated versions of the original source (near duplicates). Nevertheless, this document sharing facilitates the spread of abusive content (e.g., child pornography), copyright infringement and, in some cases, defamatory content, adversely affecting the public image of people or corporations (e.g., defamatory images of politicians and celebrities, people in embarrassing situations, etc.). Several researchers have successfully developed approaches for the detection and recognition of near-duplicate documents, aiming at identifying similar copies of a given multimedia document (e.g., image, video, etc.) published on the Internet. Notwithstanding, only recently some researches have developed approaches that go beyond the near-duplicate detection task and aim at finding the ancestral relationship between the near duplicates and the original source of a document. For this, the development of approaches for calculating the dissimilarity between near duplicates and correctly reconstruct structures that represent the relationship between them automatically is required. This problem is referred to in the literature as Multimedia Phylogeny. Solutions for multimedia phylogeny can help researchers to solve problems in forensics, content-based document retrieval and illegal-content document tracking, for instance. In this thesis, we designed and developed approaches to solve the phylogeny reconstruction problem for digital images and videos. Considering images, we proposed approaches to deal with the phylogeny problem considering two main points: (i) the forest reconstruction, an important task when we consider scenarios in which there is a set of semantically similar images, but generated by different sources or at different times; and (ii) new measures for dissimilarity calculation between near-duplicates, given that the dissimilarity calculation directly impacts the quality of the phylogeny reconstruction. The results obtained with our approaches for image phylogeny showed effective, identifying the root of the forests (original images of an evolution sequence) with accuracy up to 95%. For video phylogeny, we developed a new approach for temporal alignment in the video sequences before calculating the dissimilarity between them, once that, in real-world conditions, a pair of videos can be temporally misaligned, one video can have some frames removed and video compression can be applied, for example. For such problem, the proposed methods yield up to 87% correct of accuracy for finding the roots of the treesDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/05815-2FAPESPCAPE

Cultural Context-Aware Models and IT Applications for the Exploitation of Musical Heritage

Information engineering has always expanded its scope by inspiring innovation in different scientific disciplines. In particular, in the last sixty years, music and engineering have forged a strong connection in the discipline known as “Sound and Music Computing”. Musical heritage is a paradigmatic case that includes several multi-faceted cultural artefacts and traditions. Several issues arise from the analog-digital transfer of cultural objects, concerning their creation, preservation, access, analysis and experiencing. The keystone is the relationship of these digitized cultural objects with their carrier and cultural context. The terms “cultural context” and “cultural context awareness” are delineated, alongside the concepts of contextual information and metadata. Since they maintain the integrity of the object, its meaning and cultural context, their role is critical. This thesis explores three main case studies concerning historical audio recordings and ancient musical instruments, aiming to delineate models to preserve, analyze, access and experience the digital versions of these three prominent examples of musical heritage. The first case study concerns analog magnetic tapes, and, in particular, tape music, a particular experimental music born in the second half of the XX century. This case study has relevant implications from the musicology, philology and archivists’ points of view, since the carrier has a paramount role and the tight connection with its content can easily break during the digitization process or the access phase. With the aim to help musicologists and audio technicians in their work, several tools based on Artificial Intelligence are evaluated in tasks such as the discontinuity detection and equalization recognition. By considering the peculiarities of tape music, the philological problem of stemmatics in digitized audio documents is tackled: an algorithm based on phylogenetic techniques is proposed and assessed, confirming the suitability of these techniques for this task. Then, a methodology for a historically faithful access to digitized tape music recordings is introduced, by considering contextual information and its relationship with the carrier and the replay device. Based on this methodology, an Android app which virtualizes a tape recorder is presented, together with its assessment. Furthermore, two web applications are proposed to faithfully experience digitized 78 rpm discs and magnetic tape recordings, respectively. Finally, a prototype of web application for musicological analysis is presented. This aims to concentrate relevant part of the knowledge acquired in this work into a single interface. The second case study is a corpus of Arab-Andalusian music, suitable for computational research, which opens new opportunities to musicological studies by applying data-driven analysis. The description of the corpus is based on the five criteria formalized in the CompMusic project of the University Pompeu Fabra of Barcelona: purpose, coverage, completeness, quality and re-usability. Four Jupyter notebooks were developed with the aim to provide a useful tool for computational musicologists for analyzing and using data and metadata of such corpus. The third case study concerns an exceptional historical musical instrument: an ancient Pan flute exhibited at the Museum of Archaeological Sciences and Art of the University of Padova. The final objective was the creation of a multimedia installation to valorize this precious artifact and to allow visitors to interact with the archaeological find and to learn its history. The case study provided the opportunity to study a methodology suitable for the valorization of this ancient musical instrument, but also extendible to other artifacts or museum collections. Both the methodology and the resulting multimedia installation are presented, followed by the assessment carried out by a multidisciplinary group of experts