Biometric recognition systems require the development of new technologies and need improvement. A new filter of Ateb-Gabor has been investigated. The filter is based on the use of a combination of Gabor filter and periodic Ateb-functions. This combination allows us to provide flexibility for control by choosing two parameters m and n, which is provided by the mathematical apparatus of the Ateb functions. Filtration was performed on the example of biometric images. It has been shown that Ateb-Gabor filtration gives better filtration results than ordinary Gabor filter. Experimental research has been carried out.

/pdf/research-of-ateb-gabor-filter-in-biometric-protection-s1z1r3ikuc.pdf

Research of Ateb-Gabor Filter in Biometric Protection Systems

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).

https://dl.acm.org/doi/pdf/10.1145/3357384.3358034

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

In this paper, a Computer-generated graphics (CG) facial image regeneration scheme for anti-forensics based on generative adversarial network (CGR-GAN) is proposed. The generator of CGR-GAN utilizes a deep U-Net structure, and its discriminator utilizes some stacked convolution layers. Besides, content loss and style loss are both designed to guarantee that the regenerated CG facial images (CGR) retain both the facial profile of the original CG and the characteristics of natural image (NI). Experimental results and analysis demonstrate that the CG facial images regenerated by the proposed anti-forensics scheme can achieve better visual quality compared with those of the existing CG facial image anti-forensics and domain adaptation methods, and it can strike a good balance between visual quality and deception ability.

CGR-GAN: CG Facial Image Regeneration for Antiforensics Based on Generative Adversarial Network

Gait anonymization for protecting a person’s identity against gait recognition while maintaining naturalness is a new research direction. It can be used to protect the identity of people in videos to be posted on social networks, in police videos that require redaction, and in videos obtained from surveillance systems. We have developed a spatio-temporal generative adversarial network (ST-GAN) that uses random noise synthesized in the gait distribution to generate anonymized gaits that appear natural. ST-GAN consists of a generator that uses the original gait and random noise to generate an anonymized gait and two discriminators, a spatial discriminator and a temporal discriminator, to estimate the probability that a gait is the original one and not an anonymized one. Evaluation showed that the anonymized gaits generated with the proposed method are more natural than those generated with an existing method and that the proposed method outperforms the existing method in preventing gaits from being recognized by a gait recognition system.

/pdf/spatio-temporal-generative-adversarial-network-for-gait-2zycb4ufgp.pdf

Spatio-temporal generative adversarial network for gait anonymization

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.

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

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.

/pdf/identifying-computer-generated-text-using-statistical-8r7n60rb4k.pdf

Identifying computer-generated text using statistical analysis

The human gait has become another biometrie trait used in security systems because it is unique to each person and can be recognized at a distance. However, a bad actor could use a gait recognition system to identify a person on the basis of his or her gait. We have developed a gait anonymization method that prevents unauthorized gait recognition. It modifies the gait so that the person cannot be identified while maintaining the naturalness of the gait. The modification is done by adding another gait, called "noise gait". A convolutional neural network makes this modification by taking two gaits as input, the original gait and the noise gait, and outputting an anonymized gait. The proposed method was evaluated using the success rate and mean opinion score (MOS). The success rate is the rate of failed gait recognition, and the MOS is a measure of the naturalness of the anonymized gait. In our experiments, the success rate achieved 98.86% at most while the highest naturalness score is 3.73 in the MOS scale. These findings should open new research directions regarding privacy protection related to gait recognition.

/pdf/an-approach-for-gait-anonymization-using-deep-learning-2tb43ivxso.pdf

An approach for gait anonymization using deep learning

Gait anonymization while maintaining naturalness is used for protecting a person's identity against gait recognition systems when a video of the person walking is uploaded to social media. There has been some research on gait anonymization, but only for high-quality silhouette gaits. We present an RGB gait anonymization model for low-quality silhouette gaits that can generate natural, seamless anonymized gaits for which the original silhouettes cannot be extracted correctly. Our model includes two main networks. The first one, a deep convolutional generative adversarial network, is used to anonymize the original gait by adding to it a random noise vector. By training on high-quality silhouette data, this network can generate a high-quality anonymized silhouette sequence from a low-quality silhouette one. Restricting its input to binary silhouette sequences instead of color gaits forces it to focus on anonymizing the gait rather than changing body color. The second main network, which follows the first one, colorizes the anonymized silhouette sequence generated by the first network by using the color of the original gait. Evaluation in terms of success rate and naturalness demonstrated that our model can anonymize gaits while maintaining naturalness.

An RGB Gait Anonymization Model for Low-Quality Silhouettes

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.

/pdf/transformation-on-computer-generated-facial-image-to-avoid-413lwehf48.pdf

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.

/pdf/identifying-computer-translated-paragraphs-using-coherence-478pohx17i.pdf

Ngoc-Dung T. Tieu

Papers

Identifying computer-generated text using statistical analysis

An approach for gait anonymization using deep learning

An RGB Gait Anonymization Model for Low-Quality Silhouettes

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

Identifying Computer-Translated Paragraphs using Coherence Features