The increasing use of Artificial Intelligence(AI) technologies, such as Large LanguageModels (LLMs) has led to nontrivial improvementsin various tasks, including accurate authorshipidentification of documents. However,while LLMs improve such defense techniques,they also simultaneously provide a vehicle formalicious actors to launch new attack vectors.To combat this security risk, we evaluate theadversarial robustness of authorship models(specifically an authorship verification model)to potent LLM-based attacks. These attacksinclude untargeted methods - authorship obfuscationand targeted methods - authorshipimpersonation. For both attacks, the objectiveis to mask or mimic the writing style of an authorwhile preserving the original texts’ semantics,respectively. Thus, we perturb an accurateauthorship verification model, and achievemaximum attack success rates of 92% and 78%for both obfuscation and impersonation attacks,respectively.

Generalization in Visual Question Answering (VQA) requires models to answer questions about images with contexts beyond the training distribution. Existing attempts primarily refine unimodal aspects, overlooking enhancements in multimodal aspects. Besides, diverse interpretations of the input lead to various modes of answer generation, highlighting the role of causal reasoning between interpreting and answering steps in VQA. Through this lens, we propose Cognitive pathways VQA (CopVQA) improving the multimodal predictions by emphasizing causal reasoning factors. CopVQA first operates a pool of pathways that capture diverse causal reasoning flows through interpreting and answering stages. Mirroring human cognition, we decompose the responsibility of each stage into distinct experts and a cognition-enabled component (CC). The two CCs strategically execute one expert for each stage at a time. Finally, we prioritize answer predictions governed by pathways involving both CCs while disregarding answers produced by either CC, thereby emphasizing causal reasoning and supporting generalization. Our experiments on real-life and medical data consistently verify that CopVQA improves VQA performance and generalization across baselines and domains. Notably, CopVQA achieves a new state-of-the-art (SOTA) on the PathVQA dataset and comparable accuracy to the current SOTA on VQA-CPv2, VQAv2, and VQA- RAD, with one-fourth of the model size.

Authorship verification is used to link texts written by the same author without needing a model per author, making it useful to deanonymizing users spreading text with malicious intent. In this work, we evaluated our Cross-Encoder system with four Transformers using differently tuned variants of fanfiction data and found that our BigBird pipeline outperformed Longformer, RoBERTa, and ELECTRA and performed competitively against the official top ranked system from the PAN evaluation. We also examined the effect of authors and fandoms not seen in training on model performance. Through this, we found fandom has the greatest influence on true trials, and that a balanced training dataset in terms of class and fandom performed the most consistently.