DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization

Deepfake technology has rapidly advanced, posing significant threats to information integrity and societal trust. While significant progress has been made in detecting deepfakes, the simultaneous manipulation of audio and visual modalities, sometimes at small parts but still altering the meaning, presents a more challenging detection scenario. We present a novel audio-visual deepfake detection framework that leverages the inter-modality differences in machine perception of speech, based on the assumption that in real samples - in contrast to deepfakes - visual and audio signals coincide in terms of information. Our framework leverages features from deep networks that specialize in video and audio speech recognition to spot frame-level cross-modal incongruities, and in that way to temporally localize the deepfake forgery. To this end, DiMoDif employs a Transformer encoder-based architecture with a feature pyramid scheme and local attention, and optimizes the detection model through a composite loss function accounting for frame-level detections and fake intervals localization. DiMoDif outperforms the state-of-the-art on the Temporal Forgery Localization task by +47.88% AP@0.75 on AV-Deepfake1M, and performs on-par on LAV-DF. On the Deepfake Detection task, it outperforms the state-of-the-art by +30.5% AUC on AV-Deepfake1M, +2.8% AUC on FakeAVCeleb, and performs on-par on LAV-DF. Code available at https://github.com/mever-team/dimodif.

IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition

Deep learning techniques have been successfully applied in Synthetic Aperture Radar (SAR) target recognition in static scenarios relying on predefined datasets. However, in real-world scenarios, models must incrementally learn new information without forgetting previously learned knowledge. Models' tendency to forget old knowledge when learning new tasks, known as catastrophic forgetting, remains an open challenge. In this paper, an incremental learning framework, called IncSAR, is proposed to mitigate catastrophic forgetting in SAR target recognition. IncSAR comprises a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in individual branches combined through a late-fusion strategy. A denoising module, utilizing the properties of Robust Principal Component Analysis (RPCA), is introduced to alleviate the speckle noise present in SAR images. Moreover, a random projection layer is employed to enhance the linear separability of features, and a Linear Discriminant Analysis (LDA) approach is proposed to decorrelate the extracted class prototypes. Experimental results on the MSTAR and OpenSARShip benchmark datasets demonstrate that IncSAR outperforms state-of-the-art approaches, leading to an improvement from $98.05\%$ to $99.63\%$ in average accuracy and from $3.05\%$ to $0.33\%$ in performance dropping rate.

Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images

Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the performance of synthetic image detectors as deceptive synthetic images evolve throughout their online lifespan. Our study reveals that, despite advancements in the field, current state-of-the-art detectors struggle to distinguish between synthetic and real images in the wild. Moreover, we show that the time elapsed since the initial online appearance of a synthetic image negatively affects the performance of most detectors. Ultimately, by employing a retrieval-assisted detection approach, we demonstrate the feasibility to maintain initial detection performance throughout the whole online lifespan of an image and enhance the average detection efficacy across several state-of-the-art detectors by 6.7% and 7.8% for balanced accuracy and AUC metrics, respectively.

BAdd: Bias Mitigation through Bias Addition

Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations, they fail in complex real-world scenarios. In particular, existing methods excel in controlled experiments involving benchmarks with single-attribute injected biases, but struggle with multi-attribute biases being present in well-established CV datasets. Here, we introduce BAdd, a simple yet effective method that allows for learning fair representations invariant to the attributes introducing bias by incorporating features representing these attributes into the backbone. BAdd is evaluated on seven benchmarks and exhibits competitive performance, surpassing state-of-the-art methods on both single- and multi-attribute benchmarks. Notably, BAdd achieves +27.5% and +5.5% absolute accuracy improvements on the challenging multi-attribute benchmarks, FB-Biased-MNIST and CelebA, respectively.

TextureCrop: Enhancing Synthetic Image Detection through Texture-based Cropping

Generative AI technologies produce hyper-realistic imagery that can be used for nefarious purposes such as producing misleading or harmful content, among others. This makes Synthetic Image Detection (SID) an essential tool for defending against AI-generated harmful content. Current SID methods typically resize input images to a fixed resolution or perform center-cropping due to computational concerns, leading to challenges in effectively detecting artifacts in high-resolution images. To this end, we propose TextureCrop, a novel image pre-processing technique. By focusing on high-frequency image parts where generation artifacts are prevalent, TextureCrop effectively enhances SID accuracy while maintaining manageable memory requirements. Experimental results demonstrate a consistent improvement in AUC across various detectors by 5.7% compared to center cropping and by 14% compared to resizing, across high-resolution images from the Forensynths and Synthbuster datasets.

Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?

Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to support false narratives. Recent research in evidence-based OOC detection has seen a trend towards increasingly complex architectures, incorporating Transformers, foundation models, and large language models. In this study, we introduce a simple yet robust baseline, which assesses MUltimodal SimilaritiEs (MUSE), specifically the similarity between image-text pairs and external image and text evidence. Our results demonstrate that MUSE, when used with conventional classifiers like Decision Tree, Random Forest, and Multilayer Perceptron, can compete with and even surpass the state-of-the-art on the NewsCLIPpings and VERITE datasets. Furthermore, integrating MUSE in our proposed "Attentive Intermediate Transformer Representations" (AITR) significantly improved performance, by 3.3% and 7.5% on NewsCLIPpings and VERITE, respectively. Nevertheless, the success of MUSE, relying on surface-level patterns and shortcuts, without examining factuality and logical inconsistencies, raises critical questions about how we define the task, construct datasets, collect external evidence and overall, how we assess progress in the field. We release our code at: https://github.com/stevejpapad/outcontext-misinfo-progress

TGIF: Text-Guided Inpainting Forgery Dataset

Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, making sophisticated image edits possible with minimal effort. This poses new challenges for digital media forensics. For example, diffusion model-based approaches could either splice the inpainted region into the original image, or regenerate the entire image. In the latter case, traditional image forgery localization (IFL) methods typically fail. This paper introduces the Text-Guided Inpainting Forgery (TGIF) dataset, a comprehensive collection of images designed to support the training and evaluation of image forgery localization and synthetic image detection (SID) methods. The TGIF dataset includes approximately 80k forged images, originating from popular open-source and commercial methods; SD2, SDXL, and Adobe Firefly. Using this data, we benchmark several state-of-the-art IFL and SID methods. Whereas traditional IFL methods can detect spliced images, they fail to detect regenerated inpainted images. Moreover, traditional SID may detect the regenerated inpainted images to be fake, but cannot localize the inpainted area. Finally, both types of methods fail when exposed to stronger compression, while they are less robust to modern compression algorithms, such as WEBP. As such, this work demonstrates the inefficiency of state-of-the-art detectors on local manipulations performed by modern generative approaches, and aspires to help with the development of more capable IFL and SID methods. The dataset can be downloaded at https://github.com/IDLabMedia/tgif-dataset.

Evaluating AI Group Fairness: a Fuzzy Logic Perspective

Artificial intelligence systems often address fairness concerns by evaluating and mitigating measures of group discrimination, for example that indicate biases against certain genders or races. However, what constitutes group fairness depends on who is asked and the social context, whereas definitions are often relaxed to accept small deviations from the statistical constraints they set out to impose. Here we decouple definitions of group fairness both from the context and from relaxation-related uncertainty by expressing them in the axiomatic system of Basic fuzzy Logic (BL) with loosely understood predicates, like encountering group members. We then evaluate the definitions in subclasses of BL, such as Product or Lukasiewicz logics. Evaluation produces continuous instead of binary truth values by choosing the logic subclass and truth values for predicates that reflect uncertain context-specific beliefs, such as stakeholder opinions gathered through questionnaires. Internally, it follows logic-specific rules to compute the truth values of definitions. We show that commonly held propositions standardize the resulting mathematical formulas and we transcribe logic and truth value choices to layperson terms, so that anyone can answer them. We also use our framework to study several literature definitions of algorithmic fairness, for which we rationalize previous expedient practices that are non-probabilistic and show how to re-interpret their formulas and parameters in new contexts.

Towards Standardizing AI Bias Exploration

Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns as measures of bias that they aim to constrain or mitigate. In practice, one should explore a wide variety of (sometimes incompatible) measures before deciding which ones warrant corrective action, but their narrow scope means that most new situations can only be examined after devising new measures. In this work, we present a mathematical framework that distils literature measures of bias into building blocks, hereby facilitating new combinations to cover a wide range of fairness concerns, such as classification or recommendation differences across multiple multi-value sensitive attributes (e.g., many genders and races, and their intersections). We show how this framework generalizes existing concepts and present frequently used blocks. We provide an open-source implementation of our framework as a Python library, called FairBench, that facilitates systematic and extensible exploration of potential bias concerns.

Sampling Strategies for Mitigating Bias in Face Synthesis Methods

Synthetically generated images can be used to create media content or to complement datasets for training image analysis models. Several methods have recently been proposed for the synthesis of high-fidelity face images; however, the potential biases introduced by such methods have not been sufficiently addressed. This paper examines the bias introduced by the widely popular StyleGAN2 generative model trained on the Flickr Faces HQ dataset and proposes two sampling strategies to balance the representation of selected attributes in the generated face images. We focus on two protected attributes, gender and age, and reveal that biases arise in the distribution of randomly sampled images against very young and very old age groups, as well as against female faces. These biases are also assessed for different image quality levels based on the GIQA score. To mitigate bias, we propose two alternative methods for sampling on selected lines or spheres of the latent space to increase the number of generated samples from the under-represented classes. The experimental results show a decrease in bias against underrepresented groups and a more uniform distribution of the protected features at different levels of image quality.