arXiv subjects

Qiwei Tian

Publications and source records attributed to Qiwei Tian.

9 recordsLinked to original sources

arXiv2025

Pay Less Attention to Function Words for Free Robustness of Vision-Language Models

To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks, and propose Function-word De-Attention (FDA) accordingly to mitigate the impact of function words. Similar to differential amplifiers, our FDA calculates the original and the function-word cross-attention within attention heads, and differentially subtracts the latter from the former for more aligned and robust VLMs. Comprehensive experiments include 2 SOTA baselines under 6 different attacks on 2 downstream tasks, 3 datasets, and 3 models. Overall, our FDA yields an average 18/13/53% ASR drop with only 0.2/0.3/0.6% performance drops on the 3 tested models on retrieval, and a 90% ASR drop with a 0.3% performance gain on visual grounding. We demonstrate the scalability, generalization, and zero-shot performance of FDA experimentally, as well as in-depth ablation studies and analysis. Code is available at https://github.com/michaeltian108/FDA.

cs.LG↗

arXiv2025

Adversarial Video Promotion Against Text-to-Video Retrieval

Thanks to the development of cross-modal models, text-to-video retrieval (T2VR) is advancing rapidly, but its robustness remains largely unexamined. Existing attacks against T2VR are designed to push videos away from queries, i.e., suppressing the ranks of videos, while the attacks that pull videos towards selected queries, i.e., promoting the ranks of videos, remain largely unexplored. These attacks can be more impactful as attackers may gain more views/clicks for financial benefits and widespread (mis)information. To this end, we pioneer the first attack against T2VR to promote videos adversarially, dubbed the Video Promotion attack (ViPro). We further propose Modal Refinement (MoRe) to capture the finer-grained, intricate interaction between visual and textual modalities to enhance black-box transferability. Comprehensive experiments cover 2 existing baselines, 3 leading T2VR models, 3 prevailing datasets with over 10k videos, evaluated under 3 scenarios. All experiments are conducted in a multi-target setting to reflect realistic scenarios where attackers seek to promote the video regarding multiple queries simultaneously. We also evaluated our attacks for defences and imperceptibility. Overall, ViPro surpasses other baselines by over $30/10/4\%$ for white/grey/black-box settings on average. Our work highlights an overlooked vulnerability, provides a qualitative analysis on the upper/lower bound of our attacks, and offers insights into potential counterplays. Code will be publicly available at https://github.com/michaeltian108/ViPro.

cs.CV↗

arXiv2025

Kagome goldene with flat bands and Dirac nodal line fermions via line-graph epitaxy

The kagome lattice has emerged as a promising platform for investigating exotic quantum phases. However, achieving a single-atomic-layer kagome lattice in elemental materials remains a significant challenge. Here, we introduce line-graph epitaxy, a novel approach that enables the atomic-scale synthesis of goldene, a monolayer of elemental gold atoms arranged in a kagome lattice. Through scanning tunneling microscopy/spectroscopy (STM/STS), and density functional theory (DFT) calculations, we demonstrate the formation of kagome goldene, featuring a flat band with a van Hove singularity approximately 1.1 eV below the Fermi level, signaling strong electron correlation effects. Notably, the flat band is disrupted at the zigzag edges of goldene nanoflakes, revealing substantial edge effects. Furthermore, our calculations show that weak interlayer interactions between goldene and the underlying Au2Ge substrate generate dual Dirac nodal lines through a proximity effect. These findings offer not only a novel strategy for constructing elemental kagome lattices, but also a generalizable framework for fabricating and controlling line-graph materials. This research advances the exploration of quantum phases driven by strong correlations and the design of materials for next-generation quantum technologies.

cond-mat.mes-hall↗

arXiv2023

Layer-dependent evolution of electronic structures and correlations in rhombohedral multilayer graphene

The recent discovery of superconductivity and magnetism in trilayer rhombohedral graphene (RG) establishes an ideal, untwisted platform to study strong correlation electronic phenomena. However, the correlated effects in multilayer RG have received limited attention, and, particularly, the evolution of the correlations with increasing layer number remains an unresolved question. Here, we show the observation of layer-dependent electronic structures and correlations, under surprising liquid nitrogen temperature, in RG multilayers from 3 to 9 layers by using scanning tunneling microscopy and spectroscopy. We explicitly determine layer-enhanced low-energy flat bands and interlayer coupling strengths. The former directly demonstrates the further flattening of low-energy bands in thicker RG, and the latter indicates the presence of varying interlayer interactions in RG multilayers. Moreover, we find significant splittings of the flat bands, ranging from ~50-80 meV, at 77 K when they are partially filled, indicating the emergence of interaction-induced strongly correlated states. Particularly, the strength of the correlated states is notably enhanced in thicker RG and reaches its maximum in the six-layer, validating directly theoretical predictions and establishing abundant new candidates for strongly correlated systems. Our results provide valuable insights into the layer dependence of the electronic properties in RG and demonstrate it as a suitable system for investigating robust and highly accessible correlated phases.

cond-mat.mes-hall↗

arXiv2023

Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval

Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. However, existing studies in deep metric learning (DML) still suffer from two major limitations: weak adversary and model collapse. In this paper, we address these two limitations by proposing Collapse-Aware TRIplet DEcoupling (CA-TRIDE). Specifically, TRIDE yields a stronger adversary by spatially decoupling the perturbation targets into the anchor and the other candidates. Furthermore, CA prevents the consequential model collapse, based on a novel metric, collapseness, which is incorporated into the optimization of perturbation. We also identify two drawbacks of the existing robustness metric in image retrieval and propose a new metric for a more reasonable robustness evaluation. Extensive experiments on three datasets demonstrate that CA-TRIDE outperforms existing defense methods in both conventional and new metrics. Codes are available at https://github.com/michaeltian108/CA-TRIDE.

cs.CV↗

arXiv2023

Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning

Transfer-based adversarial attacks raise a severe threat to real-world deep learning systems since they do not require access to target models. Adversarial training (AT), which is recognized as the strongest defense against white-box attacks, has also guaranteed high robustness to (black-box) transfer-based attacks. However, AT suffers from heavy computational overhead since it optimizes the adversarial examples during the whole training process. In this paper, we demonstrate that such heavy optimization is not necessary for AT against transfer-based attacks. Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL). Our experimental results show that DRL outperforms widely-used AT techniques (e.g., PGD-AT, TRADES, EAT, and FAT) in terms of black-box robustness and even surpasses the top-1 defense on RobustBench when combined with diverse data augmentations and loss regularizations. We also identify other benefits of DRL, for instance, the model generalization capability and robust fairness.

cs.CR↗

arXiv2023

Nanopore-patterned CuSe drives the realization of PbSe-CuSe lateral heterostructure

Monolayer PbSe has been predicted to be a two-dimensional (2D) topological crystalline insulator (TCI) with crystalline symmetry-protected Dirac-cone-like edge states. Recently, few-layered epitaxial PbSe has been grown on the SrTiO3 substrate successfully, but the corresponding signature of the TCI was only observed for films not thinner than seven monolayers, largely due to interfacial strain. Here, we demonstrate a two-step method based on molecular beam epitaxy for the growth of the PbSe-CuSe lateral heterostructure on the Cu(111) substrate, in which we observe a nanopore patterned CuSe layer that acts as the template for lateral epitaxial growth of PbSe. This further results in a monolayer PbSe-CuSe lateral heterostructure with an atomically sharp interface. Scanning tunneling microscopy and spectroscopy measurements reveal a four-fold symmetric square lattice of such monolayer PbSe with a quasi-particle band gap of 1.8 eV, a value highly comparable with the theoretical value of freestanding PbSe. The weak monolayer-substrate interaction is further supported by both density functional theory (DFT) and projected crystal orbital Hamilton population, with the former predicting the monolayer's anti-bond state to reside below the Fermi level. Our work demonstrates a practical strategy to fabricate a high-quality in-plane heterostructure, involving a monolayer TCI, which is viable for further exploration of the topology-derived quantum physics and phenomena in the monolayer limit.

cond-mat.mtrl-sci↗

arXiv2023

Native Pb vacancy defects induced p-type characteristic in epitaxial monolayer PbSe

PbSe, a predicted two-dimensional (2D) topological crystalline insulator (TCI) in the monolayer limit, possess excellent thermoelectric and infrared optical properties. Native defects in PbSe take a crucial role for the applications. However, little attention has been paid to the defect induced doping characteristics. Here, we provide an experimental and theoretical investigation of defects induced p-type characteristic on epitaxial monolayer PbSe on Au(111). Scanning tunneling microscopy (STM) measurements demonstrate an epitaxial PbSe monolayer with a fourfold symmetric lattice. Combined scanning tunneling spectroscopy (STS) and density functional theory (DFT) calculations reveal a quasi-particle bandgap of 0.8eV of PbSe. STM results unveil that there are two types of defects on the surface, one is related the vacancies of Pb atoms and the other is the replacement of the absent Se atoms by Pb. Corresponding theoretical optimization confirms the structures of the defects. More importantly, both STS measurements and DFT calculations give evidence that the Pb vacancies move the Fermi energy inside the valence band and produce extra holes, leading to p-type characteristics of PbSe. Our work provides effective information for the future research of device performance based on PbSe films.

cond-mat.mtrl-sci↗

arXiv2023

Moiré-induced bandgap tuning by varying electric dipole in InSe/CuSe vertical heterostructure

The stacked two layered materials with a lattice constant mismatch and/or with twist angle relative to each other can create a moiré pattern, modulating the electronic properties of the pristine materials. Here, we combine scanning tunneling microscopy/spectroscopy and density functional theory calculations to investigate the moiré potential induced bandgap tuning in InSe/CuSe vertical heterostructure synthesized by a two-step of molecular beam epitaxy. Scanning tunneling microscopy measurements demonstrate the heterostructure with a superlattice periodicity of ~3.48nm and a twist angle of about 11° between the monolayers. Scanning tunneling spectroscopy record on the different stacking sites of the heterostructure reveals the bandgap of the InSe is location-dependent and a variation of 400 meV is observed. Density functional theory calculations reveal that the moiré-induce electric dipole in the monolayer InSe is the key factor for tuning the bandgap. Besides, charge transfer between CuSe and InSe also contributes to the bandgap variation due to its stacking related. We also show that the moiré potential not only can tune the bandgap of InSe but also can vanish the Dirac nodal line of CuSe in some stackings. Our explorations provide valuable information in understanding the electronic properties of the twodimensional moiré materials.

cond-mat.mtrl-sci↗