Deep hashing models have been widely adopted to tackle the challenges of large-scale image retrieval. However, these approaches face serious security risks due to their vulnerability to adversarial examples. Despite the increasing exploration of targeted attacks on deep hashing models, existing approaches still suffer from a lack of multimodal guidance, reliance on labeling information and dependence on pixel-level operations for attacks. To address these limitations, we proposed DiffHash, a novel diffusion-based targeted attack for deep hashing. Unlike traditional pixel-based attacks that directly modify specific pixels and lack multimodal guidance, our approach focuses on optimizing the latent representations of images, guided by text information generated by a Large Language Model (LLM) for the target image. Furthermore, we designed a multi-space hash alignment network to align the high-dimension image space and text space to the low-dimension binary hash space. During reconstruction, we also incorporated text-guided attention mechanisms to refine adversarial examples, ensuring them aligned with the target semantics while maintaining visual plausibility. Extensive experiments have demonstrated that our method outperforms state-of-the-art (SOTA) targeted attack methods, achieving better black-box transferability and offering more excellent stability across datasets.

The IIR (infinite-impulse response) filter design problem in the complex domain is formulated such that the existence of a best approximation is guaranteed. A modified Ellacott-Williams algorithm (1976) is proposed to solve the design problem. The proposed algorithm uses a complex linear approximation utilized for FIR (finite-impulse response) filter design and it usually converges in several iterations. Design examples are included.>

The frequency response of a finite-duration impulse response (FIR) filter is degraded by unknown variations in the values of the filter coefficients. If the filter coefficients can only be specified to within a certain percentage accuracy, the solution of a nested optimization problem gives a filter design such that over all possible coefficient variations, the worst frequency response is as good as possible in the Chebyshev sense. The size of the nested optimization problem grows exponentially with the length of the filter. However a tractable optimization problem can be solved whose solution gives a suboptimal filter design that is likely to be nearly as good as the globally optimal solution.