Coreference resolution in Persian, a task critical to natural language understanding, presents unique challenges due to the language's pro-drop tendencies, flexible word order, and rich morphosyntactic agreement system. This study introduces the first end-to-end (e-2-e) neural architecture for comprehensive Persian coreference resolution, encompassing pronominal, nominal, and named entity mentions. The system leverages ParsBERT and innovatively integrates Joint Mention Detection and Type Classification (JMDTC), an Adaptive Antecedent Pruning Threshold (AAPT), Morphosyntactically-Informed Attention (MIA), and Cross-Segment Coreference with Global Context Aggregation (CS-GCA). By jointly optimizing mention detection and antecedent linking, the system surpasses traditional pipelined approaches, eliminating the need for handcrafted features and complex syntactic parsers. A CoNLL average F1-score of 76.16% was achieved by the system on the Mehr corpus, which represents a 4.03-point improvement compared with the previous state-of-the-art. Furthermore, it demonstrates robust generalization, achieving a CoNLL average F1-score of 74.20% on the RCDAT corpus (evaluated using the Uppsala test set). These findings facilitate scalable coreference resolution in low-resource languages presenting similar morphosyntactic challenges.

Objective image quality assessment (IQA) aims to predict human judgment on an image’s visual quality. This computational model is applicable in various scenarios, such as monitoring, image compression and image restoration. However, the research in this area began with natural scenes while image contents has become much more diverse. Remote computing and screen sharing applications led to the prevalence of screen content images (SCIs), which are full of texts, flat areas, and also natural scenes. Due to their different characteristics, traditional methods for IQA are insufficient for SCIs. In this paper, we show that capturing the distortions in the horizontal and vertical structures of an SCI is effective for predicting its quality. We also take into account the human visual system’s trend to analyze a scene in a multi-scale fashion. Based on these observations, we propose wavelet analysis as a means to accomplish the mentioned strategies. Experimental results on three SCI quality datasets show that plausible predictions can be made by the proposed full-reference method, while having an acceptable time complexity.

The Encrypted Image Reversible Data Hiding (EIRDH) is a hot area of research in multimedia outsourcing scenarios. In such a setting, a content owner possesses confidential images that he wants to upload to an untrusted server. The server is supposed to embed a secret message in the images before sending them to potential receivers, and the receivers expect to recover the original images without any distortion. EIRDH schemes are designed to meet reversible data hiding goals while preserving the confidentiality of the host images. Confidentiality is provided by means of encryption algorithms; however, these algorithms, in turn, introduce the challenge of dealing with ciphered images in both embedding and extraction phases. The current paper tries to propose a privacy-preserving reversible data hiding scheme for JPEG images without the need for any levels of decompression. We propose an elliptic curve-based asymmetric, commutative, and separable EIRDH method, in which no entities other than the sender and the eligible receiver of the hidden message (embedded in the encrypted image) are able to recognize its existence. In particular, even the server that performs the embedding remains completely unaware of the content of the images. An important security feature of our scheme involves the security of its multiple encryptions against Chosen-Plaintext Attack (CPA), which is an attack model that assumes the adversary can obtain the encryptions of the images on his choice to achieve possible extra information to attack. Experimental results show that our scheme achieves full reversibility and a satisfactory level of capacity, while it accurately extracts all the hidden bits.

In this paper, we propose a novel copy-move forgery detection scheme that can accurately localize duplicated regions with a reasonable computational cost. To this end, a new interest point detector is proposed utilizing the advantages of both block-based and traditional keypoint-based methods. The detected keypoints adaptively cover the entire image, even low contrast regions, based on a uniqueness metric. Moreover, a new filtering algorithm is employed, which can effectively prune the falsely matched regions. Considering the new interest point detector, an iterative improvement strategy is proposed. The whole procedure is iterated along with adjusting the keypoints density based on the achieved information. The experimental results demonstrate that the proposed scheme outperforms the state-of-the-art methods using two public benchmark databases.

This paper proposes a new method for digital video watermarking in compressed domain. Both the embedding and extracting phases are performed after entropy decoding. Consequently, fully decompressing the compressed video is not necessary making this scheme an appropriate choice for real-time application. Furthermore, taking the structural information into account leads to presenting a robust watermarking scheme along with less quality degradation. To select suitable coefficients for embedding the watermark, three different aspects, imperceptibility, security, and bit rate increase, have been considered. These performance factors are adjusted by defining three priority matrices. In addition, a content based key is proposed in order to overcome the collusion attack. The flexibility of our method to provide desired characteristic can be expressed as another advantage.

In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.