Synthetic image source attribution is a challenging task, especially in data scarcity conditions requiring few-shot or zero-shot classification capabilities. We present a new training-free one-shot attribution method based on image resynthesis. A prompt describing the image under analysis is generated, then it is used to resynthesize the image with all the candidate sources. The image is attributed to the model which produced the resynthesis closest to the original image in a proper feature space. We also introduce a new dataset for synthetic image attribution consisting of face images from commercial and open-source text-to-image generators. The dataset provides a challenging attribution framework, useful for developing new attribution models and testing their capabilities on different generative architectures. The dataset structure allows to test approaches based on resynthesis and to compare them to few-shot methods. Results from state-of-the-art few-shot approaches and other baselines show that the proposed resynthesis method outperforms existing techniques when only a few samples are available for training or fine-tuning. The experiments also demonstrate that the new dataset is a challenging one and represents a valuable benchmark for developing and evaluating future few-shot and zero-shot methods.
AI-generated speech is becoming increasingly used in everyday life, powering virtual assistants, accessibility tools, and other applications. However, it is also being exploited for malicious purposes such as impersonation, misinformation, and biometric spoofing. As speech deepfakes become nearly indistinguishable from real human speech, the need for robust detection methods and effective countermeasures has become critically urgent. In this paper, we present the ISPL's submission to the SAFE challenge at IH MMSec 2025, where our system ranked first across all tasks. Our solution introduces a novel approach to audio deepfake detection based on a Mixture of Experts architecture. The proposed system leverages multiple state-of-the-art detectors, combining their outputs through an attention-based gating network that dynamically weights each expert based on the input speech signal. In this design, each expert develops a specialized understanding of the shared training data by learning to capture different complementary aspects of the same input through inductive biases. Experimental results indicate that our method outperforms existing approaches across multiple datasets. We further evaluate and analyze the performance of our system in the SAFE challenge.
Limited data-availability and ethical concerns regarding individual privacy hinder large-scale understanding of protests using social media data. Current visual methods for human image understanding are capable of re-identifying faces, understanding complex scenes and objects, and associating them together. This presents an ethical dilemma, as large vision models can memorize and leak sensitive information from training data. To our knowledge, unfortunately, no existing protest analysis method takes privacy into consideration. To close the gap in the literature, we propose a simple and robust framework for understanding protest dynamics with privacy preservation, namely privacy preserving protest dynamics (P3D). Our P3D framework aims to replace private data with well labeled synthetic reproductions using conditional image synthesis. Extensive evaluation of the proposed P3D method demonstrates its ability to generate realistic and diverse imagery, and its efficacy in utility and privacy for downstream protest analysis. Moreover, unlike other private models, P3D can provide a moderate level of privacy by modeling complex visual features in protest images while maintaining downstream utility. Code is available at https://github.com/cgarchbold/P3D
The Data Act’s ‘Mandatory Smart Contract Kill Switch’, officially enforced by the EU fromJanuary 11, 2024, is sparking significant debate across Europe and within the cryptocurrency community. The primaryconcern is that this mandate could potentially render existing deployed smart contracts illegal and undermine the decentralization and immutability that are fundamental to DeFi. Analysis so far indicates that the biggest issue isthe lack of clear guidelines and scope regarding the implementation of the smart contract kill switch in the DataAct. In response, this paper proposes a private blockchain-based software transaction system to address the persistentissues in the software transaction process. The proposed system aims to construct a reliable software transaction environment by presenting methods and measures for applying smart contract kill switch that can address security incidents and license violations. This is achieved through the distribution of profits based on developer contributions, calculated using blockchain technology and peer evaluation, ensuring transparency, fairness, and decentralization.
In batch steganography, the sender spreads the secret payload among multiple cover images forming a bag. The question investigated in this paper is how many and what kind of images the sender should select for her bag. We show that by forming bags with a bias towards selecting images that are more difficult to steganalyze, the sender can either lower the probability of being detected or save on bandwidth by sending a smaller bag. These improvements can be quite substantial. Our study begins with theoretical reasoning within a suitably simplified model. The findings are confirmed on experiments with real images and modern steganographic and steganalysis techniques.
