The widespread use of AI-generated text has introduced significant security concerns, driving the need for reliable detection systems. However, recent studies reveal that neural network-based detectors are vulnerable to adversarial examples. To improve the robustness of such classifiers, a number of adversarial attack strategies have been developed, particularly in the context of text sentiment classification. Most existing adversarial attack methods focus on the semantics of individual words or sentences, often neglecting the broader contextual semantics of the entire text-particularly in the case of long AI-generated text. This limitation frequently results in adversarial examples that lack fluency and coherence. In this paper, we propose a novel method calledSentence-based Adversarial attack on AI-Generated Text detectors (SAGT), which generates linguistically fluent adversarial examples by inserting model-generated sentences into the original text. To ensure contextual semantic consistency, we extract important keywords from the original text-selected based on changes in the detector's confidence score-and incorporate them into the generated sentences. Extensive experimental results demonstrate that adversarial examples crafted bySAGTcan effectively evade AI-generated text detectors.

Deep hashing has been widely used in image retrieval tasks, while deep hashing networks are vulnerable to adversarial example attacks. To improve the deep hashing networks’ robustness, it is essential to investigate adversarial attacks on the networks, especially targeted attacks. Among the existing targeted attacks for hashing, the generation-based targeted attack methods have attracted increasing attention due to their efficiency in generating adversarial examples. However, these methods supervise the generation of adversarial examples solely with the hash codes of positive samples, without employing the hash codes of all points in the training set to directly participate in supervisory training, thereby making the attack less effective. Since the hash codes of the training set samples are generated by a well-trained hashing model, these hash codes retain rich semantic information of their corresponding samples, highlighting the necessity of sufficiently utilizing them. Therefore, in this paper, we propose a targeted attack method that utilizes all points’ hash codes in the training set to guide the generation of adversarial attack examples directly. Specifically, we first decode the target label to obtain the corresponding feature map. Then, we concatenate the feature map with the query image and feed them into an encoder-decoder network that employs a skip-connection strategy to obtain a perturbed example. Furthermore, to guide adversarial example generation, we introduce a loss function that exploits the similarities between the perturbed example’s hash code and all points’ hash codes in the training set, thereby making sufficient utilization of the rich semantic information in these hash codes. Experimental results illustrate that our method outperforms the state-of-the-art targeted attack methods in targeted attack effectiveness and transferability. The code is available athttps://github.com/rongxintu3/APGA.

Generated text detectors can effectively detect the machine-generated texts which aim to produce false information to destroy the credibility of the media platform. However, generated text detectors are vulnerable to adversarial example attacks which focus on char-level perturbations to produce many word errors. In this paper, we design a sentence granularity based black-box attack model Sentence-Keyword-Attack (SK-Attack), which can effectively generate semantics-preserved, fluent, and grammatical adversarial examples. SK-Attack adaptively truncates the input examples based on sentence granularity and searches for the essential sentences to apply a sequence of contextualized perturbations with strict constraints. SK-Attack also applies keyword protection to preserve the keywords from being perturbed. Experiments show that SK-Attack outperforms the baselines when attacking the RoBERTa detector with various challenging generated text datasets and also has strong transferability to other attack models.

With the explosively increasing of academic papers, efficient academic document retrieval is becoming an essential requirement for large-scale information retrieval systems. Inspired by the success of deep semantic hashing in normal document retrieval, deep semantic hashing is a promising approach for academic document retrieval by mapping academic documents into efficient hash codes. However, for academic document retrieval, the existing deep semantic hashing methods suffer from following two problems: (1) they cannot differentiate the importance of different field labels; (2) they cannot plenty utilize the structure information in paper citations. To address these problems, we propose a novel Large-scale Academic deep Semantic Hashing, called LASH. Specifically, LASH first treats paper citations as a citation network, and then employs a multi-input variational deep autoencoder to directly encode both structure information of the citation network and semantic information of academic documents into unified hash codes. Moreover, a weighted percentage similarity is designed to measure the importance of different field labels, which is a linear combination of Jaccard and Cosine similarity. Supervised by the similarity, the learned unified hash codes can further preserve the importance of different field labels. Extensive experiments show LASH significantly outperforms state-of-the-art baselines over proposed three real-world large-scale academic datasets.

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-modal hashing methods have attracted considerable attention from the researchers. For the supervised cross-modal hashing methods, how to make the learned hash codes sufficiently preserve semantic information contained in the label of datapoints is the key to further enhance the retrieval performance. Hence, almost all supervised cross-modal hashing methods usually depend on defining similarities between datapoints with the label information to guide the hashing model learning fully or partly. However, the defined similarity between datapoints can only capture the label information of datapoints partially and misses abundant semantic information, which then hinders the further improvement of retrieval performance. Thus, in this paper, different from previous works, we propose a novel cross-modal hashing method without defining the similarity between datapoints, called Deep Cross-modal Proxy Hashing (DCPH). Specifically, DCPH first trains a proxy hashing network to transform each category information of a dataset into a semantic discriminative hash code, called proxy hash code. Each proxy hash code can preserve the semantic information of its corresponding category well. Next, without defining the similarity between datapoints to supervise the training process of the modality-specific hashing networks, we propose a novelmargin-dynamic-softmax lossto directly utilize the proxy hashing codes as supervised information. Finally, by minimizing the novelmargin-dynamic-softmax loss, the modality-specific hashing networks can be trained to generate hash codes that can simultaneously preserve the cross-modal similarity and abundant semantic information well. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the cross-modal retrieval tasks.

Cloud platforms provide a good stage for storing and sharing big image data for users, although some privacy issues arise. Image encryption technology can prevent privacy leakage and can ensure secure image data sharing on cloud platforms. Hence, in this paper, an unequal encryption scheme based on saliency detection is proposed. First, based on the mechanism of visual perception and the theory of feature integration, the visual attention model is employed to realize the recognition of significant regions and insignificant regions. Then, a dynamic DNA encryption algorithm is proposed to exploit heavyweight encryption for significant regions, while semi-tensor product compressed sensing is introduced to exploit lightweight encryption and compression for insignificant regions. Experimental results demonstrate that the proposed framework can serve to secure big image data services.