arXiv:2511.00427v1 Announce Type: new 
Abstract: With the rapid development of generative models, detecting generated fake images to prevent their malicious use has become a critical issue recently. Existing methods frame this challenge as a naive binary image classification task. However, such methods focus only on visual clues, yielding trained detectors susceptible to overfitting specific image patterns and incapable of generalizing to unseen models. In this paper, we address this issue from a multi-modal perspective and find that fake images cannot be properly aligned with corresponding captions compared to real images. Upon this observation, we propose a simple yet effective detector termed ITEM by leveraging the image-text misalignment in a joint visual-language space as discriminative clues. Specifically, we first measure the misalignment of the images and captions in pre-trained CLIP's space, and then tune a MLP head to perform the usual detection task. Furthermore, we propose a hierarchical misalignment scheme that first focuses on the whole image and then each semantic object described in the caption, which can explore both global and fine-grained local semantic misalignment as clues. Extensive experiments demonstrate the superiority of our method against other state-of-the-art competitors with impressive generalization and robustness on various recent generative models.

تتناول ورقة جديدة على arXiv القضية الملحة للكشف عن الصور المزيفة التي تم إنشاؤها بواسطة نماذج متقدمة. مع تطور هذه التقنيات، يزداد احتمال استخدامها بشكل ضار، مما يجعل الكشف الفعال أمرًا بالغ الأهمية. يجادل المؤلفون بأن الطرق التقليدية، التي تعتمد فقط على الأدلة البصرية، غالبًا ما تفشل في التعميم عبر نماذج مختلفة. من خلال تقديم نهج هرمي لعدم توافق الصورة والنص، يهدفون إلى تعزيز قدرات الكشف، مما يضمن أن الأنظمة يمكن أن تحدد وتخفف المخاطر التي تطرحها الصور الاصطناعية بشكل أفضل. هذه الأبحاث مهمة لأنها تتناول قضية متزايدة حول نزاهة الوسائط الرقمية.

Un nuevo artículo en arXiv aborda el problema urgente de detectar imágenes falsas generadas por modelos avanzados. A medida que estas tecnologías evolucionan, el potencial de uso indebido aumenta, lo que hace que la detección efectiva sea crucial. Los autores argumentan que los métodos tradicionales, que se basan únicamente en pistas visuales, a menudo no logran generalizarse a diferentes modelos. Al introducir un enfoque jerárquico para la desalineación imagen-texto, buscan mejorar las capacidades de detección, asegurando que los sistemas puedan identificar y mitigar mejor los riesgos que presentan las imágenes sintéticas. Esta investigación es significativa ya que aborda una preocupación creciente sobre la integridad de los medios digitales.

Un nouvel article sur arXiv aborde la question pressante de la détection des images générées par des modèles avancés. À mesure que ces technologies évoluent, le potentiel d'utilisation abusive augmente, rendant la détection efficace cruciale. Les auteurs soutiennent que les méthodes traditionnelles, qui s'appuient uniquement sur des indices visuels, échouent souvent à se généraliser à différents modèles. En introduisant une approche hiérarchique de la désalignement image-texte, ils visent à améliorer les capacités de détection, garantissant que les systèmes peuvent mieux identifier et atténuer les risques posés par les images synthétiques. Cette recherche est significative car elle répond à une préoccupation croissante concernant l'intégrité des médias numériques.

A new paper on arXiv tackles the pressing issue of detecting fake images generated by advanced models. As these technologies evolve, the potential for misuse increases, making effective detection crucial. The authors argue that traditional methods, which rely solely on visual cues, often fail to generalize across different models. By introducing a hierarchical approach to image-text misalignment, they aim to enhance detection capabilities, ensuring that systems can better identify and mitigate the risks posed by synthetic images. This research is significant as it addresses a growing concern in digital media integrity.

Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection

arXiv:2505.18227v3 Announce Type: replace 
Abstract: In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped to an embedding, enabling parallel attention computations while preserving the input's essential information. Due to the quadratic computational complexity of transformer self-attention mechanisms, token reduction has primarily been used as an efficiency strategy. This is especially true in single vision and language domains, where it helps balance computational costs, memory usage, and inference latency. Despite these advances, this paper argues that token reduction should transcend its traditional efficiency-oriented role in the era of large generative models. Instead, we position it as a fundamental principle in generative modeling, critically influencing both model architecture and broader applications. Specifically, we contend that across vision, language, and multimodal systems, token reduction can: (i) facilitate deeper multimodal integration and alignment, (ii) mitigate "overthinking" and hallucinations, (iii) maintain coherence over long inputs, and (iv) enhance training stability, etc. We reframe token reduction as more than an efficiency measure. By doing so, we outline promising future directions, including algorithm design, reinforcement learning-guided token reduction, token optimization for in-context learning, agentic framework design, and broader ML and scientific domains.

يبرز مقال حديث أن تقليل الرموز في هياكل المحولات يجب أن يتجاوز مجرد الكفاءة، داعيًا إلى دوره كمبدأ أساسي في النمذجة التوليدية عبر مجالات متعددة، بما في ذلك الرؤية واللغة.

Un artículo reciente enfatiza que la reducción de tokens en arquitecturas Transformer debería ir más allá de la mera eficiencia, abogando por su papel como un principio fundamental en la modelización generativa en varios dominios, incluyendo visión y lenguaje.

Un article récent souligne que la réduction des tokens dans les architectures Transformer devrait aller au-delà de la simple efficacité, plaidant pour son rôle en tant que principe fondamental dans la modélisation générative à travers divers domaines, y compris la vision et le langage.

A recent paper emphasizes that token reduction in Transformer architectures should extend beyond mere efficiency, advocating for its role as a fundamental principle in generative modeling across various domains, including vision and language.

Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality

One More Thing in AI – Your Shortcut to AI Mastery

Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection

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