{'fraction_non_alphanumeric': 0.026052104208416832, 'fraction_numerical': 0.008016032064128256, 'mean_word_length': 5.091463414634147, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adversarial Examples in Arabic

Several studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples - perturbed inputs that cause DNN-based models to produce incorrect outputs. A variety of adversarial attacks have been proposed in the domains of computer vision and natural language processing (NLP); however, most attacks in the NLP domain have been applied to DNNs that were trained on English corpora. This paper proposes the first set of black-box adversarial attacks designed to perturb Arabic textual inputs. By intentionally violating the noun-adjective agreement in Arabic, two state-of-the-art DNN architectures are successfully fooled in the task of sentiment analysis, and classification accuracy was reduced by an average of 52.97% for the word-level BiLSTM model and 50.44% for the word-level CNN model. We believe that our findings will encourage other researchers to investigate the robustness of DNNs when applied to natural languages beyond English.