In recent years, the study on defending deep hashing models against adversarial attacks has garnered increasing attention. Among them, adversarial training is an effective method to train robust deep hashing models. Existing adversarial training methods for deep hashing simultaneously optimize original deep hashing loss and proposed adversarial training loss to train a robust model. However, we argue that directly using the original deep hashing loss will guide the model to learn excessive non-robust patterns from clean examples when extracting discriminative semantic information, thereby limiting model robustness. To tackle this, we propose a novel Clean model Representation Distillation based Adversarial Training (CRDAT) method, which enables the robust model to learn both discriminative semantic information and robust patterns by separating these two losses into two stages, i.e., standard training stage of a clean teacher model and adversarial training stage of a robust student model. Specifically, we propose a novel representation distillation based adversarial training loss, which distills the representations of the teacher model on clean examples at both the hash code level and feature level to guide the student model's learning on adversarial examples. Extensive experiments on multiple datasets and deep hashing methods demonstrate that our CRDAT method can greatly improve model robustness and achieve state-of-the-art defense performance.

With the extensive utilization of deep hashing, there exists a surging interest in studying adversarial attacks against it. Previous methods have demonstrated the superior white-box attack performance against deep hashing. However, the more challenging and realistic targeted black-box attack has not yet been explored sufficiently, which will result in an over-estimation on model robustness. In this paper, we focus on targeted black-box attack based on transferability, and propose a novel Targeted Transferable Attack method against deep hashing with Generative Adversarial Network (TTA-GAN). Specifically, we first propose a new Iterative Anchor code Optimization (IAO) method to generate anchor code with superior representative semantics of target label, which can improve both targeted white-box and black-box performances. Then, we propose a generation-based method to directly generate targeted transferable adversarial example by training a conditional generator and a discriminator. Moreover, to further promote the targeted transferability, we conduct multiple input transformations on the generated adversarial example to alleviate the overfitting phenomenon on source model. Finally, we extend our method to a novel model ensemble attack method TTA-GANens to preserve the representative semantics on multiple models, specialized for deep hashing. Extensive experiments demonstrate the superior targeted black-box attack performance than the state-of-the-art methods.

Cross-modal retrieval between videos and texts has attracted growing attention due to the rapid growth of user-generated videos on the web. To solve this problem, most approaches try to learn a joint embedding space to measure the cross-modal similarities, while paying little attention to the representation of each modality. Video is more complicated than the commonly used visual feature, since the audio and caption on the screen also contain rich information. Recently, the aggregations of multiple features in videos boost the benchmark of the video-text retrieval system. However, they usually handle each feature independently, which ignores the interchange of high-level semantic relations among these multiple features. Moreover, despite the inter-modal ranking constraint where semantically-similar texts and videos should stay closer, the modality-specific requirement, i.e. two similar videos/texts should have similar representations, is also significant. In this paper, we propose a novel Multi-Feature Graph ATtention Network (MFGATN) for cross-modal video-text retrieval. Specifically, we introduce a multi-feature graph attention module, which enriches the representation of each feature in videos with the interchange of high-level semantic information among them. Moreover, we elaborately design a novel Dual Constraint Ranking Loss (DCRL), which simultaneously considers the inter-modal ranking constraint and the intra-modal structure constraint to preserve both the cross-modal semantic similarity and the modality-specific consistency in the embedding space. Experiments on two datasets, i.e. MSR-VTT and MSVD, demonstrate that our method achieves significant performance gain compared with the state-of-the-arts.

Hashing has become increasingly important for large-scale image retrieval, of which the low storage cost and fast searching are two key properties. However, existing methods adopt large neural networks, which are hard to be deployed in resource-limited devices due to the unacceptable memory and runtime overhead. We address that this huge overhead of neural networks somewhatviolates the appealing properties of hashing. In this paper, we propose a novel deep hashing method, called Binary Neural Network Hashing (BNNH) for fast image retrieval. Specifically, we construct an efficient binarized network architecture to provide lighter model and faster inference, which directly generates binary outputs as the desired hash codes without introducing the quantization loss. Besides, in order to circumvent the huge performance degradation caused by the extremely quantized activations, we introduce a simple yet effective activation-aware loss to explicitly guide the updating of activations in intermediate layers. Extensive experiments conducted on three benchmarks show that the proposed method outperforms the state-of-the-art binarization methods by large margins and validate the efficiency of BNNH.

Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to its low storage consumption and fast retrieval speed. In real cross-modal retrieval applications, it's hard to obtain label information. Recently, increasing attention has been paid to unsupervised cross-modal hashing. However, existing methods fail to exploit the intrinsic connections between images and their corresponding descriptions or tags (text modality). In this paper, we propose a novel Deep Semantic-Alignment Hashing (DSAH) for unsupervised cross-modal retrieval, which sufficiently utilizes the co-occurred image-text pairs. DSAH explores the similarity information of different modalities and we elaborately design a semantic-alignment loss function, which elegantly aligns the similarities between features with those between hash codes. Moreover, to further bridge the modality gap, we innovatively propose to reconstruct features of one modality with hash codes of the other one. Extensive experiments on three cross-modal retrieval datasets demonstrate that DSAH achieves the state-of-the-art performance.