Isao Echi Zen

Bio: Isao Echi Zen is an academic researcher from National Institute of Informatics. The author has contributed to research in topics: Automatic summarization & Parsing. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Identifying computer-generated text using statistical analysis

Abstract: Computer-based automatically generated text is used in various applications (e.g., text summarization, machine translation) and has come to play an important role in daily life. However, computer-generated text may produce confusing information due to translation errors and inappropriate wording caused by faulty language processing, which could be a critical issue in presidential elections and product advertisements. Previous methods for detecting computer-generated text typically estimate text fluency, but this may not be useful in the near future due to the development of neural-network-based natural language generation that produces wording close to human-crafted wording. A different approach to detecting computergenerated text is thus needed. We hypothesize that human-crafted wording is more consistent than that of a computer. For instance, Zipf's law states that the most frequent word in human-written text has approximately twice the frequency of the second most frequent word, nearly three times that of the third most frequent word, and so on. We found that this is not true in the case of computer-generated text. We hence propose a method to identify computer-generated text on the basis of statistics. First, the word distribution frequencies are compared with the corresponding Zipfian distributions to extract the frequency features. Next, complex phrase features are extracted because human-generated text contains more complex phrases than computer-generated text. Finally, the higher consistency of the human-generated text is quantified at both the sentence level using phrasal verbs and at the paragraph level using coreference resolution relationships, which are integrated into consistency features. The combination of the frequencies, the complex phrases, and the consistency features was evaluated for 100 English books written originally in English and 100 English books translated from Finnish. The results show that our method achieves better performance (accuracy = 98.0%; equal error rate = 2.9%) compared with the most suitable method for books using parsing tree feature extraction. Evaluation using two other languages (French and Dutch) showed similar results. The proposed method thus works consistently in various languages.

Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures

Abstract: User reviews have become a cornerstone of how we make decisions. However, this user-based feedback is susceptible to manipulation as recent research has shown the feasibility of automatically generating fake reviews. Previous investigations, however, have focused on generative fake review approaches that are (i) domain dependent and not extendable to other domains without replicating the whole process from scratch; and (ii) character-level based known to generate reviews of poor quality that are easily detectable by anti-spam detectors and by end users. In this work, we propose and evaluate a new class of attacks on online review platforms based on neural language models at word-level granularity in an inductive transfer-learning framework wherein a universal model is refined to handle domain shift, leading to potentially wide-ranging attacks on review systems. Through extensive evaluation, we show that such model-generated reviews can bypass powerful anti-spam detectors and fool end users. Paired with this troubling attack vector, we propose a new defense mechanism that exploits the distributed representation of these reviews to detect model-generated reviews. We conclude that despite the success of neural models in generating realistic reviews, our proposed RNN-based discriminator can combat this type of attack effectively (90% accuracy).

Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Abstract: Machine-generated text is increasingly difficult to distinguish from text authored by humans. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine-generated text is a key countermeasure for reducing the abuse of NLG models, and presents significant technical challenges and numerous open problems. We provide a survey that includes 1) an extensive analysis of threat models posed by contemporary NLG systems and 2) the most complete review of machine-generated text detection methods to date. This survey places machine-generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models. While doing so, we highlight the importance that detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

Assisting academics to identify computer generated writing

Abstract: ABSTRACT Authentic writing is an important aspect in education and research. Unfortunately, academic misconduct occurs among students and researchers. Consequently, written articles undergo certain detection measures and most teaching and research institutions use a range of software to detect plagiarism. However, state-of-the-art Automatic Article Generator (AAG) writing powered by Artificial Intelligence provides a new platform for new types of serious academic misconduct that cannot be easily detected and even if they are detected, can be hard to prove. The main objective of this study is to raise awareness of these tools among academics. This paper first explains the features of AAG writing, then investigates whether academics can distinguish AAG writing from human writing and whether raising the awareness of AAG between academics can improve their ability to detect AAG writing. A case study showed how difficult it is for academics with no knowledge of AAGs to identify this writing. A survey was used to indicate how a training session can improve the ability of detecting AAG writing. The results show that raising awareness training increased the academics’ ability to detect AAG writing. Lastly, the possible solutions to mitigate the academic integrity issues associated with AAG writing have been discussed.

Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers

Abstract: The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of current state-of-the-art models, but despite a developing threat landscape, there has been minimal analysis of the robustness of detection methods to adversarial attacks. To this end, we evaluate neural and non-neural approaches on their ability to detect computer-generated text, their robustness against text adversarial attacks, and the impact that successful adversarial attacks have on human judgement of text quality. We find that while statistical features underperform neural features, statistical features provide additional adversarial robustness that can be leveraged in ensemble detection models. In the process, we find that previously effective complex phrasal features for detection of computer-generated text hold little predictive power against contemporary generative models, and identify promising statistical features to use instead. Finally, we pioneer the usage of ΔMAUVE as a proxy measure for human judgement of adversarial text quality.

Detecting Machine-Translated Paragraphs by Matching Similar Words.

Abstract: Machine-translated text plays an important role in modern life by smoothing communication from various communities using different languages. However, unnatural translation may lead to misunderstanding, a detector is thus needed to avoid the unfortunate mistakes. While a previous method measured the naturalness of continuous words using a N-gram language model, another method matched noncontinuous words across sentences but this method ignores such words in an individual sentence. We have developed a method matching similar words throughout the paragraph and estimating the paragraph-level coherence, that can identify machine-translated text. Experiment evaluates on 2000 English human-generated and 2000 English machine-translated paragraphs from German showing that the coherence-based method achieves high performance (accuracy = 87.0%; equal error rate = 13.0%). It is efficiently better than previous methods (best accuracy = 72.4%; equal error rate = 29.7%). Similar experiments on Dutch and Japanese obtain 89.2% and 97.9% accuracy, respectively. The results demonstrate the persistence of the proposed method in various languages with different resource levels.