Vision Transformers (ViTs) have experienced significant progress and are quantized for deployment in resource-constrained applications. Quantized models are vulnerable to targeted bit-flip attacks (BFAs). A targeted BFA prepares a trigger and a corresponding Trojan/backdoor, inserting the latter (with RowHammer bit flipping) into a victim model, to mislead its classification on samples containing the trigger. Existing targeted BFAs on quantized ViTs are limited in that: (1) they require numerous bit-flips, and (2) the separation between flipped bits is below 4 KB, making attacks infeasible with RowHammer in real-world scenarios. We propose a new and practical targeted attack Flip-S against quantized ViTs. The core insight is that in quantized models, a scale factor change ripples through a batch of model weights. Consequently, flipping bits in scale factors, rather than solely in model weights, enables more cost-effective attacks. We design a Scale-Factor-Search (SFS) algorithm to identify critical bits in scale factors for flipping, and adopt a mutual exclusion strategy to guarantee a 4 KB separation between flips. We evaluate Flip-S on CIFAR-10 and ImageNet datasets across five ViT architectures and two quantization levels. Results show that Flip-S achieves attack success rate (ASR) exceeding 90.0% on all models with 50 bits flipped, outperforming baselines with ASR typically below 80.0%. Furthermore, compared to the SOTA, Flip-S reduces the number of required bit-flips by 8×-20× while reaching equal or higher ASR. Our source code is publicly available1.

In this paper, a fast Fourier transform (FFT)-enhanced low-complexity super-resolution sensing algorithm for near-field source localization with both angle and range estimation is proposed. Most traditional near-field source localization algorithms suffer from excessive computational complexity or incompatibility with existing array architectures. To address such issues, this paper proposes a novel near-field sensing algorithm that combines coarse and fine granularity of spectrum peak search. Specifically, a spectral pattern in the angle domain is first constructed using FFT to identify potential angles where sources are present. Afterwards, a 1D beamforming is performed in the distance domain to obtain potential distance regions. Finally, a refined 2D multiple signal classification (MUSIC) is conducted within each narrowed angle-distance region to estimate the precise location of the sources. Numerical results demonstrate that the proposed algorithm can significantly reduce the computational complexity of 2D spectrum peak searches and achieve target localization with high-resolution.