This work introduces PICASSO, a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images. PICASSO converts a given CAD sketch image into parametric primitives that can be seamlessly integrated into CAD software. Our framework leverages rendering self-supervision to enable the pre-training of a CAD sketch parameterization network using sketch renderings only, thereby eliminating the need for corresponding CAD parameterization. Thus, we significantly reduce reliance on parameter-level annotations, which are often unavailable, particularly for hand-drawn sketches. The two primary components of PICASSO are (1) a Sketch Parameterization Network (SPN) that predicts a series of parametric primitives from CAD sketch images, and (2) a Sketch Rendering Network (SRN) that renders parametric CAD sketches in a differentiable manner and facilitates the computation of a rendering (image-level) loss for self-supervision. We demonstrate that the proposed PICASSO can achieve reasonable performance even when finetuned with only a small number of parametric CAD sketches. Extensive evaluation on the widely used SketchGraphs [37] and CAD as Language [14] datasets validates the effectiveness of the proposed approach on zero- and few-shot learning scenarios.

This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Net-work (LAA-Net). Existing methods for high-quality deep-fake detection are mainly based on a supervised binary classifier coupled with an implicit attention mechanism. As a result, they do not generalize well to unseen ma-nipulations. To handle this issue, two main contributions are made. First, an explicit attention mechanism within a multi-task learning framework is proposed. By combining heatmap-based and self-consistency attention strate-gies, LAA-Net is forced to focus on a few small artifact-prone vulnerable regions. Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and ef-fective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy. Experiments performed on sev-eral benchmarks show the superiority of our approach in terms of Area Under the Curve (AUC) and Average Preci-sion (AP). The code is available at https://github.com/10Ring/LAA-Net.

This paper addresses the challenge of temporal deepfake localization. Instead of classifying entire videos as real or fake, the goal is isolating forged frames in untrimmed videos that might be partially manipulated. Recently, few deepfake localization methods have emerged. They are mostly supervised, therefore relying on costly annotations and suffering from a lack of generalization to unseen manipulations. As an alternative, we propose reformulating deepfake localization as an unsupervised time-series anomaly detection problem. Hence, to investigate the relevance of the proposed formulation, recent state-of-the-art techniques in anomaly detection for timeseries are evaluated in the context of deepfake localization. To avoid using large architectures, geometric representations, e.g., facial landmarks, are used as input. Moreover, a facialregion based ensembling strategy is introduced for a better modelling of localized deepfake artifacts. Experiments performed on the ForgeryNet dataset demonstrate the effectiveness of the proposed ensembling method and highlight the suitability of the suggested formulation.

peer reviewed

This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG. Existing methods are generally trained in a supervised manner at the classification level, focusing on detecting at most two types of forgeries; thus, limiting their generalization capability across different deepfake types. To handle that, we reformulate the deepfake detection problem as a one-class classification supported by a self-supervision mechanism. Our intuition is that by estimating the distribution of real data in a discriminative feature space, deepfakes can be detected as outliers regardless of their type. UNTAG involves two sequential steps. First, deep representations are learned based on a self-supervised pretext task focusing on manipulated regions. Second, a oneclass classifier fitted on authentic image embeddings is used to detect deepfakes. The results reported on several datasets show the effectiveness of UNTAG and the relevance of the proposed new paradigm. The code is publicly available.

In this paper, we investigate the suitability of current multi-label classification approaches for deepfake detection. With the recent advances in generative modeling, new deepfake detection methods have been proposed. Nevertheless, they mostly formulate this topic as a binary classification problem, resulting in poor explainability capabilities. Indeed, a forged image might be induced by multi-step manipulations with different properties. For a better interpretability of the results, recognizing the nature of these stacked manipulations is highly relevant. For that reason, we propose to model deepfake detection as a multi-label classification task, where each label corresponds to a specific kind of manipulation. In this context, state-of-the-art multi-label image classification methods are considered. Extensive experiments are performed to assess the practical use case of deepfake detection.

In the past years, RGB-based deepfake detection has shown notable progress thanks to the development of effective deep neural networks. However, the performance of deepfake detectors remains primarily dependent on the quality of the forged content and the level of artifacts introduced by the forgery method. To detect these artifacts, it is often necessary to separate and analyze the frequency components of an image. In this context, we propose to utilize the high-frequency components of color images by introducing an end-to-end trainable module that (a) extracts features from high-frequency components and (b) fuses them with the features of the RGB input. The module not only exploits the high-frequency anomalies present in manipulated images but also can be used with most RGB-based deepfake detectors. Experimental results show that the proposed approach boosts the performance of state-of-the-art networks, such as XceptionNet and EfficientNet, on a challenging deepfake dataset.