Detecting sarcasm in social media is fundamentally different from general VLM benchmarks: it is a pragmatic contradiction problem in which the literal signal in one modality is intentionally misaligned with the intended meaning, while dominant pre-training (e.g., CLIP-style contrastive agreement) biases models toward modality alignment rather than incongruity detection. We present SCARF, a contradiction-aware framework that equips large multimodal models with explicit sarcasm cues and context-sensitive retrieval. SCARF constructs coarse scene cues and fine localized evidence via tag-constrained QA, then distills them with visual tokens into a [FUSION] control vector for the LLM; a label-contrastive retriever supplies type- and context-matched exemplars, and a local multi-view encoder surfaces micro-cues. With the same backbone and training data, SCARF attains 87.92% Acc/86.67% F1 on MMSD2.0 and 77.14% Acc/76.44% F1 zero-shot on XDMSD, outperforming a comparably fine-tuned LLaVA-1.5. Ablations show sarcasm clue fusion is the main driver of gains, and tag-constrained QA improves rationale grounding and reduces hallucinations.

This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques to assess the vulnerability of various types of deep learning models to adversarial examples. This tutorial will particularly highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs). We will also introduce some effective countermeasures to improve robustness of deep learning models, with a particular focus on adversarial training. We aim to provide a comprehensive overall picture about this emerging direction and enable the community to be aware of the urgency and importance of designing robust deep learning models in safety-critical data analytical applications, ultimately enabling the end-users to trust deep learning classifiers. We will also summarize potential research directions concerning the adversarial robustness of deep learning, and its potential benefits to enable accountable and trustworthy deep learning-based data analytical systems and applications.

The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However, current methods for universal adversarial attacks are based on additive perturbation, which cause misclassification when the perturbation is directly added to the input images. In this paper, for the first time, we show that a universal adversarial attack can also be achieved via non-additive perturbation (e.g., spatial transformation). More importantly, to unify both additive and non-additive perturbations, we propose a novel unified yet flexible framework for universal adversarial attacks, called GUAP, which is able to initiate attacks by additive perturbation, non-additive perturbation, or the combination of both. Extensive experiments are conducted on ImageNet dataset with several deep neural network models including GoogLeNet, VGG and ResNet. The empirical experiments demonstrate that GUAP can obtain up to 99.24% successful attack rate on ImageNet dataset, leading to over 19% improvements than current state-of-the-art universal adversarial attacks. The code for reproducing the experiments in this paper is available at https://github.com/TrustAI/GUAP.