7 research outputs found
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation
Nowadays, forgery faces pose pressing security concerns over fake news,
fraud, impersonation, etc. Despite the demonstrated success in intra-domain
face forgery detection, existing detection methods lack generalization
capability and tend to suffer from dramatic performance drops when deployed to
unforeseen domains. To mitigate this issue, this paper designs a more general
fake face detection model based on the vision transformer(ViT) architecture. In
the training phase, the pretrained ViT weights are freezed, and only the
Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center
Loss(SCL) is applied to supervise the training process, further improving the
generalization capability of the model. The proposed method achieves
state-of-the-arts detection performances in both cross-manipulation and
cross-dataset evaluations
Forgery-aware Adaptive Vision Transformer for Face Forgery Detection
With the advancement in face manipulation technologies, the importance of
face forgery detection in protecting authentication integrity becomes
increasingly evident. Previous Vision Transformer (ViT)-based detectors have
demonstrated subpar performance in cross-database evaluations, primarily
because fully fine-tuning with limited Deepfake data often leads to forgetting
pre-trained knowledge and over-fitting to data-specific ones. To circumvent
these issues, we propose a novel Forgery-aware Adaptive Vision Transformer
(FA-ViT). In FA-ViT, the vanilla ViT's parameters are frozen to preserve its
pre-trained knowledge, while two specially designed components, the Local-aware
Forgery Injector (LFI) and the Global-aware Forgery Adaptor (GFA), are employed
to adapt forgery-related knowledge. our proposed FA-ViT effectively combines
these two different types of knowledge to form the general forgery features for
detecting Deepfakes. Specifically, LFI captures local discriminative
information and incorporates these information into ViT via
Neighborhood-Preserving Cross Attention (NPCA). Simultaneously, GFA learns
adaptive knowledge in the self-attention layer, bridging the gap between the
two different domain. Furthermore, we design a novel Single Domain Pairwise
Learning (SDPL) to facilitate fine-grained information learning in FA-ViT. The
extensive experiments demonstrate that our FA-ViT achieves state-of-the-art
performance in cross-dataset evaluation and cross-manipulation scenarios, and
improves the robustness against unseen perturbations
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face
recognition system by presenting spoofed faces. State-of-the-art FAS techniques
predominantly rely on deep learning models but their cross-domain
generalization capabilities are often hindered by the domain shift problem,
which arises due to different distributions between training and testing data.
In this study, we develop a generalized FAS method under the Efficient
Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained
Vision Transformer models for the FAS task. During training, the adapter
modules are inserted into the pre-trained ViT model, and the adapters are
updated while other pre-trained parameters remain fixed. We find the
limitations of previous vanilla adapters in that they are based on linear
layers, which lack a spoofing-aware inductive bias and thus restrict the
cross-domain generalization. To address this limitation and achieve
cross-domain generalized FAS, we propose a novel Statistical Adapter
(S-Adapter) that gathers local discriminative and statistical information from
localized token histograms. To further improve the generalization of the
statistical tokens, we propose a novel Token Style Regularization (TSR), which
aims to reduce domain style variance by regularizing Gram matrices extracted
from tokens across different domains. Our experimental results demonstrate that
our proposed S-Adapter and TSR provide significant benefits in both zero-shot
and few-shot cross-domain testing, outperforming state-of-the-art methods on
several benchmark tests. We will release the source code upon acceptance
Dynamic regulation of CD28 conformation and signaling by charged lipids and ions
CD28 provides an essential co-stimulatory signal for T cell activation and its function
is critical in antitumor immunity. However, the molecular mechanism of CD28
transmembrane signaling remains elusive. Here, we find that CD28 conformation and
signaling are regulated by two counteractive charged factors, i.e. acidic phospholipid
and Ca2+ ion. NMR structure shows that acidic phospholipids can sequester CD28
signaling motifs within the membrane, thus limiting CD28 basal signaling. T-cell
receptor (TCR) activation induces local [Ca2+] increase around CD28, and Ca2+ can
use its charges to directly disrupt CD28-lipid interaction, which leads to CD28
opening and signaling. TCR, Ca2+ , and CD28 together form a dual positive feedback
circuit to significantly amplify T cell signaling and therefore increase antigen
sensitivity. This work unravels a new regulatory mechanism of CD28 signaling,
contributing to the understanding of the dependence of the co-stimulation signaling on
TCR signaling and the high sensitivity of T cells