To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics

We consider an information theoretic approach to address the problem of identifying fake digital images. We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks. Tampered images pose a serious predicament since digitized media is a ubiquitous part of our lives. These are facilitated by the easy availability of image editing software and aggravated by recent advances in deep generative models such as GANs. We propose InfoPrint, a computationally efficient solution to the IB formulation using approximate variational inference and compare it to a numerical solution that is computationally expensive. Testing on a number of standard datasets, we demonstrate that InfoPrint outperforms the state-of-the-art and the numerical solution. Additionally, it also has the ability to detect alterations made by inpainting GANs.Comment: 10 page

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them

الكشف عن تزوير الصور الرقمية باستخدام منهجية متكيفة ومُحسَّنة

الدراسة المقدمة من خلال هذا البحث تهتم بموضوع الصور الرقمية، وهي موجهة بشكلٍ خاصٍ للكشف عن التزوير الذي يمكن أن يتم تطبيقه على الصور الرقمية. إن النظام المُقترح يقدم تطبيقاً ديناميكياً متكيفاً، يستطيع الكشف عن النوعين الأكثر انتشاراً واستخداماً من التزوير وهما: تزوير النسخ، وتزوير الدمج. حيث يمكن من خلال هذا النظام الكشف عن هذين النوعين من التزوير، وذلك في أنواع وأحجام مختلفة من الصور، على عكس العديد من الدراسات السابقة والتي كانت مخصصة لنوع تزوير معين أو لصورة بمقاييس وشروط محددة. يقوم التطبيق بشكل ديناميكي بالتكيف مع الصورة المُعطاة، واختيار الخوارزمية التي تلائم هذه الصورة، بحيث يتم التوصل إلى النتيجة الأفضل في الكشف عن التزوير وذلك بما يناسب معطيات الصورة وخصائصها. كما يقدّم النظام المقترح تحسيناً فيما يتعلق بعدد الإنذارات الخاطئة التي كانت تصدر عن الأنظمة الأساسية التي يعتمد عليها التطبيق في الكشف عن تزوير النسخ، حيث أن النظامين الأساسيين المُقدمين في دراسات سابقة، كانا يعانيان من عدد كبير من الإنذارات الخاطئة والتي كانت تُظهر وجود تزوير في حين أن الصورة أصلية غير مزورة. لذلك كان أحد أهداف هذه الدراسة هو البحث عن أسباب هذه الإنذارات الخاطئة في كل طريقة على حدة، ومعالجة هذه الأسباب بهدف تحسين أداء الخوارزميات الأصلية.

Abordagem passiva para reconhecimento de adulterações em imagens digitais através da análise do padrão CFA e do BAG

A criação e a comercialização de softwares de edição de imagens permitiu que pessoas comuns pudessem realizar qualquer tipo de manipulação em imagens digitais. Em um cenário judicial, em que autenticidade e integridade dos dados são cruciais, se faz necessário técnicas que permitam garantir tais atributos. A análise forense em imagens digitais busca, através de métodos computacionais científicos, reconhecer a presença ou ausência desses atributos. O presente trabalho apresenta um método de reconhecimento de adulteração em imagens com e sem compressão JPEG. Esse método baseia-se em técnicas de análise de inconsistência do BAG (Block Artifact Grid) e do Padrão CFA (Color Filter Array) da imagem que é gerada a partir de técnicas de adulteração, tais como composição e clonagem. Os testes foram realizados em 960 imagens, utilizando as taxas de acurácia, sensibilidade, especificidade e precisão como métricas para a avaliação da efetividade do método

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future

Digital Image Tamper Detection Technique Based on Spectrum Analysis of CFA Artifacts

Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences in the distribution of acquired and interpolated pixels. The experimental evidence supports the capabilities of the proposed method for detecting a broad range of manipulations, e.g., copy-move, resizing, rotation, filtering and colorization. This technique exhibits tampered areas by computing the probability of each pixel of being interpolated and then applying the DCT on small blocks of the probability map. The value of the coefficient for the highest frequency on each block is used to decide whether the analyzed region has been tampered or not. The results shown here were obtained from tests made on a publicly available dataset of tampered images for forensic analysis. Affected zones are clearly highlighted if the method detects CFA inconsistencies. The analysis can be considered successful if the modified zone, or an important part of it, is accurately detected. By analizing a publicly available dataset with images modified with different methods we reach an 86% of accuracy, which provides a good result for a method that does not require previous training