arXiv:2501.01015v2 Announce Type: replace 
Abstract: Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of adversarial examples, including advanced optimization, data augmentation, and model modifications. However, these methods still show limited transferability, particularly in cross-architecture scenarios, such as from CNN to ViT. To achieve high transferability, we propose a technique termed Spatial Adversarial Alignment (SAA), which employs an alignment loss and leverages a witness model to fine-tune the surrogate model. Specifically, SAA consists of two key parts: spatial-aware alignment and adversarial-aware alignment. First, we minimize the divergences of features between the two models in both global and local regions, facilitating spatial alignment. Second, we introduce a self-adversarial strategy that leverages adversarial examples to impose further constraints, aligning features from an adversarial perspective. Through this alignment, the surrogate model is trained to concentrate on the common features extracted by the witness model. This facilitates adversarial attacks on these shared features, thereby yielding perturbations that exhibit enhanced transferability. Extensive experiments on various architectures on ImageNet show that aligned surrogate models based on SAA can provide higher transferable adversarial examples, especially in cross-architecture attacks.

تتناول دراسة حديثة التحديات التي تواجه الأمثلة العدائية في الشبكات العصبية العميقة، وخاصة محدودية قابليتها للنقل عبر هياكل مختلفة مثل CNN وViT. تسلط الأبحاث الضوء على استراتيجيات متعددة، مثل تحسين البيانات وزيادة البيانات، تهدف إلى تحسين هذه القابلية للنقل. فهم هذه الديناميكيات أمر بالغ الأهمية لأنه يمكن أن يؤدي إلى أنظمة ذكاء اصطناعي أكثر قوة، مما يعزز الأمان في التطبيقات التي يتم فيها نشر التعلم العميق.

Un estudio reciente discute los desafíos de los ejemplos adversariales en las redes neuronales profundas, particularmente su limitada transferibilidad entre diferentes arquitecturas como CNN y ViT. La investigación destaca varias estrategias, como la optimización y la augmentación de datos, destinadas a mejorar esta transferibilidad. Comprender estas dinámicas es crucial, ya que puede llevar a sistemas de IA más robustos, mejorando la seguridad en aplicaciones donde se utiliza el aprendizaje profundo.

Une étude récente aborde les défis des exemples adversariaux dans les réseaux de neurones profonds, en particulier leur transférabilité limitée entre différentes architectures comme les CNN et les ViT. La recherche met en lumière diverses stratégies, telles que l'optimisation et l'augmentation des données, visant à améliorer cette transférabilité. Comprendre ces dynamiques est crucial car cela peut conduire à des systèmes d'IA plus robustes, renforçant ainsi la sécurité dans les applications où l'apprentissage profond est déployé.

A recent study discusses the challenges of adversarial examples in deep neural networks, particularly their limited transferability across different architectures like CNNs and ViTs. The research highlights various strategies, such as optimization and data augmentation, aimed at improving this transferability. Understanding these dynamics is crucial as it can lead to more robust AI systems, ultimately enhancing security in applications where deep learning is deployed.

Boosting Adversarial Transferability with Spatial Adversarial Alignment

arXiv:2511.17103v1 Announce Type: new 
Abstract: Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved notable improvements by leveraging the knowledge embedded within pre-trained visual models, the lack of direct association between factual-level features and emotional categories, called the "affective gap", limits the applicability of pre-training knowledge for VER tasks. On the contrary, the explicit emotional expression and high information density in textual modality eliminate the "affective gap". Therefore, we propose borrowing the knowledge from the pre-trained textual model to enhance the emotional perception of pre-trained visual models. We focus on the factual and emotional connections between images and texts in noisy social media data, and propose Partitioned Adaptive Contrastive Learning (PACL) to leverage these connections. Specifically, we manage to separate different types of samples and devise distinct contrastive learning strategies for each type. By dynamically constructing negative and positive pairs, we fully exploit the potential of noisy samples. Through comprehensive experiments, we demonstrate that bridging the "affective gap" significantly improves the performance of various pre-trained visual models in downstream emotion-related tasks. Our code is released on https://github.com/wdqqdw/PACL.

تقدم دراسة حديثة نهجًا مبتكرًا للتعرف على المشاعر البصرية (VER) من خلال اقتراح استخدام التعلم التبايني التكيفي المقسم (PACL) لسد 'الفجوة العاطفية' بين الأنماط البصرية والنصية. تستفيد هذه الطريقة من المعرفة المستمدة من النماذج النصية المدربة مسبقًا لتعزيز قدرات الإدراك العاطفي للنماذج البصرية، خاصة في سياقات وسائل التواصل الاجتماعي المليئة بالضوضاء.

Un estudio reciente presenta un enfoque novedoso para el reconocimiento de emociones visuales (VER) al proponer el uso del Aprendizaje Contrastivo Adaptativo Partitionado (PACL) para cerrar la 'brecha afectiva' entre las modalidades visual y textual. Este método aprovecha el conocimiento de modelos textuales preentrenados para mejorar las capacidades de percepción emocional de los modelos visuales, especialmente en contextos de redes sociales ruidosas.

Une étude récente présente une approche novatrice pour la reconnaissance des émotions visuelles (VER) en proposant l'utilisation de l'apprentissage contrastif adaptatif partitionné (PACL) pour combler le 'écart affectif' entre les modalités visuelles et textuelles. Cette méthode exploite les connaissances des modèles textuels pré-entraînés pour améliorer les capacités de perception émotionnelle des modèles visuels, en particulier dans des contextes de médias sociaux bruyants.

A recent study introduces a novel approach to visual emotion recognition (VER) by proposing the use of Partitioned Adaptive Contrastive Learning (PACL) to bridge the 'affective gap' between visual and textual modalities. This method leverages knowledge from pre-trained textual models to enhance the emotional perception capabilities of visual models, particularly in noisy social media contexts.

Boosting Adversarial Transferability with Spatial Adversarial Alignment

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