Face anti-spoofing (FAS) is essential for protecting facial-biometric systems from presentation attacks. We propose WSSSP-Net, a Weakly Supervised Semantic Segmentation Plugin Network that integrates a lightweight, attention-based segmentation decoder at multiple depths of any CNN or transformer encoder. Serving only as an auxiliary training-time module, the decoder guides feature learning without increasing inference runtime. Pixel-wise spoof masks are automatically generated via a face-parsing pipeline, removing the need for manual annotations and enabling multiscale spoof-aware feature refinement. In leave-one-out evaluations on leading FAS benchmarks, WSSSP-Net reduces HTER by up to 24.9% and increases AUC by up to 3.2% over state-of-the-art methods. In out-of-distribution tests on a separate dataset, it lowers HTER by up to 18.4%. Across attack classes, it reduces average APCER by up to 12.9% and BPCER by up to 12.4%, achieving all improvements without added inference cost.

Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-Spectral Distance of 1.29 and the highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5~s audio. To extend KAN's utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates. FewSound outperforms the state-of-the-art HyperSound, with a 33.3% improvement in MSE and 60.87% in SI-SNR. These results show KAN as a robust and adaptable audio representation with the potential for scalability and integration into various hypernetwork frameworks.

Text-to-Speech (TTS) technology brings significant advantages, such as giving a voice to those with speech impairments, but also enables audio deepfakes and spoofs. The former mislead individuals and may propagate misinformation, while the latter undermine voice biometric security systems. AI-based detection can help to address these challenges by automatically differentiating between genuine and fabricated voice recordings. However, these models are only as good as their training data, which currently is severely limited due to an overwhelming concentration on English and Chinese audio in anti-spoofing databases, thus restricting its worldwide effectiveness.In response, this paper presents the Multi-Language Audio Anti-Spoof Dataset (MLAAD), created using 52 TTS models, comprising 22 different architectures, to generate 160.2 hours of synthetic voice in 23 different languages. We train and evaluate three state-of-the-art deepfake detection models with MLAAD, and observe that MLAAD demonstrates superior performance over comparable datasets like InTheWild or FakeOrReal when used as a training resource. Furthermore, in comparison with the renowned ASVspoof 2019 dataset, MLAAD proves to be a complementary resource. In tests across eight datasets, MLAAD and ASVspoof 2019 alternately outperformed each other, both excelling on four datasets.By publishing1MLAAD and making trained models accessible via an interactive webserver2, we aim to democratize antispoofing technology, making it accessible beyond the realm of specialists, thus contributing to global efforts against audio spoofing and deepfakes.

Explaining artificial intelligence models can be utilized to launch targeted adversarial attacks on text classification algorithms. Understanding the reasoning behind the model’s decisions makes it easier to prepare such samples. Most of the current text-based adversarial attacks rely on brute-force by using SHAP approach to identify the importance of tokens in the samples, we modify the crucial ones to prepare targeted attacks. We base our results on experiments using 5 datasets. Our results show that our approach outperforms TextBugger and TextFooler, achieving better results with 4 out of 5 datasets against TextBugger, and 3 out of 5 datasets against TextFooler, while minimizing perturbation introduced to the texts. In particular, we managed to outperform the efficacy of TextFooler by over 3100% and TextBugger by over 420% on the WikiPL dataset, additionally keeping high cosine similarity between the original text sample and the adversarial example. The evaluation of the results was additionally supported through a survey to assess their quality and ensure that the text perturbations did not change the intended class according to subjective, human classification.

Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news. Methods for detecting these manipulations should be characterized by good generalization and stability leading to robustness against attacks conducted with techniques that are not explicitly included in the training. In this work, we introduce Attack Agnostic Dataset - a combination of two audio DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of attacks, can lead to better generalization of detection methods. We present a thorough analysis of current DeepFake detection methods and consider different audio features (front-ends). In addition, we propose a model based on LCNN with LFCC and mel-spectrogram front-end, which not only is characterized by a good generalization and stability results but also shows improvement over LFCC-based mode - we decrease standard deviation on all folds and EER in two folds by up to 5%.

Audio DeepFakes are utterances generated with the use of deep neural networks. They are highly misleading and pose a threat due to use in fake news, impersonation, or extortion. In this work, we focus on increasing accessibility to the audio DeepFake detection methods by providing SpecRNet, a neural network architecture characterized by a quick inference time and low computational requirements. Our benchmark shows that SpecRNet, requiring up to about 40% less time to process an audio sample, provides performance comparable to LCNN architecture — one of the best audio DeepFake detection models. Such a method can not only be used by online multimedia services to verify a large bulk of content uploaded daily but also, thanks to its low requirements, by average citizens to evaluate materials on their devices. In addition, we provide benchmarks in three unique settings that confirm the correctness of our model. They reflect scenarios of low–resource datasets, detection on short utterances and limited attacks benchmark in which we take a closer look at the influence of particular attacks on given architectures.

Watermarking is an operation of embedding information into an image in a way that allows to identify ownership of the image despite applying some distortions on it. In this paper, we present a novel end-to-end solution for embedding and recovering the watermark in the digital image using convolutional neural networks. We propose a spreading method of the message over the spatial domain of the image, hence reducing the local bits per pixel capacity and significantly increasing robustness. To obtain the model we use adversarial training, apply noiser layers between the encoder and the decoder, and implement a precise JPEG approximation. Moreover, we broaden the spectrum of typically considered attacks on the watermark and we achieve high overall robustness, most notably against JPEG compression, Gaussian blur, subsampling or resizing. We show that an application of some attacks could increase robustness against other non-seen during training distortions across one group of attacks - a proper grouping of the attacks according to their scope allows to achieve high general robustness.

In this paper we consider the problem of listing all active stations in a single hop radio network in such a way that the outer adversary observing communication could not gain any significant information about the real number of stations. We also consider a counterpart of this problem such that only a good approximation of the number of activated stations is needed. This problem is motivated mainly by military applications of sensors networks, however we present how our approach can be extended to other natural problems and similar models. In our paper we present two algorithms for secure listing and size approximation of the set of activated stations. Both of them are fairly practical (in terms of volume of communication, time of execution and computational complexity) and provably secure for the assumed adversarial model.