288 research outputs found
Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering
Image forgery detection aims to detect and locate forged regions in an image.
Most existing forgery detection algorithms formulate classification problems to
classify pixels into forged or pristine. However, the definition of forged and
pristine pixels is only relative within one single image, e.g., a forged region
in image A is actually a pristine one in its source image B (splicing forgery).
Such a relative definition has been severely overlooked by existing methods,
which unnecessarily mix forged (pristine) regions across different images into
the same category. To resolve this dilemma, we propose the FOrensic ContrAstive
cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on
contrastive learning and unsupervised clustering for the image forgery
detection. Specifically, FOCAL 1) utilizes pixel-level contrastive learning to
supervise the high-level forensic feature extraction in an image-by-image
manner, explicitly reflecting the above relative definition; 2) employs an
on-the-fly unsupervised clustering algorithm (instead of a trained one) to
cluster the learned features into forged/pristine categories, further
suppressing the cross-image influence from training data; and 3) allows to
further boost the detection performance via simple feature-level concatenation
without the need of retraining. Extensive experimental results over six public
testing datasets demonstrate that our proposed FOCAL significantly outperforms
the state-of-the-art competing algorithms by big margins: +24.3% on Coverage,
+18.6% on Columbia, +17.5% on FF++, +14.2% on MISD, +13.5% on CASIA and +10.3%
on NIST in terms of IoU. The paradigm of FOCAL could bring fresh insights and
serve as a novel benchmark for the image forgery detection task. The code is
available at https://github.com/HighwayWu/FOCAL
Progressive Poisoned Data Isolation for Training-time Backdoor Defense
Deep Neural Networks (DNN) are susceptible to backdoor attacks where
malicious attackers manipulate the model's predictions via data poisoning. It
is hence imperative to develop a strategy for training a clean model using a
potentially poisoned dataset. Previous training-time defense mechanisms
typically employ an one-time isolation process, often leading to suboptimal
isolation outcomes. In this study, we present a novel and efficacious defense
method, termed Progressive Isolation of Poisoned Data (PIPD), that
progressively isolates poisoned data to enhance the isolation accuracy and
mitigate the risk of benign samples being misclassified as poisoned ones. Once
the poisoned portion of the dataset has been identified, we introduce a
selective training process to train a clean model. Through the implementation
of these techniques, we ensure that the trained model manifests a significantly
diminished attack success rate against the poisoned data. Extensive experiments
on multiple benchmark datasets and DNN models, assessed against nine
state-of-the-art backdoor attacks, demonstrate the superior performance of our
PIPD method for backdoor defense. For instance, our PIPD achieves an average
True Positive Rate (TPR) of 99.95% and an average False Positive Rate (FPR) of
0.06% for diverse attacks over CIFAR-10 dataset, markedly surpassing the
performance of state-of-the-art methods.Comment: Accepted to AAAI202
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
The heightened realism of AI-generated images can be attributed to the rapid
development of synthetic models, including generative adversarial networks
(GANs) and diffusion models (DMs). The malevolent use of synthetic images, such
as the dissemination of fake news or the creation of fake profiles, however,
raises significant concerns regarding the authenticity of images. Though many
forensic algorithms have been developed for detecting synthetic images, their
performance, especially the generalization capability, is still far from being
adequate to cope with the increasing number of synthetic models. In this work,
we propose a simple yet very effective synthetic image detection method via a
language-guided contrastive learning and a new formulation of the detection
problem. We first augment the training images with carefully-designed textual
labels, enabling us to use a joint image-text contrastive learning for the
forensic feature extraction. In addition, we formulate the synthetic image
detection as an identification problem, which is vastly different from the
traditional classification-based approaches. It is shown that our proposed
LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved
generalizability to unseen image generation models and delivers promising
performance that far exceeds state-of-the-art competitors by +22.66% accuracy
and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED
Low voltage polymer network liquid crystal for infrared spatial light modulators
We report a low-voltage and fast-response polymer network liquid crystal (PNLC) infrared phase modulator. To optimize device performance, we propose a physical model to understand the curing temperature effect on average domain size. Good agreement between model and experiment is obtained. By optimizing the UV curing temperature and employing a large dielectric anisotropy LC host, we have lowered the 2 pi phase change voltage to 22.8V at 1.55 mu m wavelength while keeping response time at about 1 ms. Widespread application of such a PNLC integrated into a high resolution liquid-crystal-on-silicon (LCoS) for infrared spatial light modulator is foreseeable
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
Online Social Networks (OSNs) have blossomed into prevailing transmission
channels for images in the modern era. Adversarial examples (AEs) deliberately
designed to mislead deep neural networks (DNNs) are found to be fragile against
the inevitable lossy operations conducted by OSNs. As a result, the AEs would
lose their attack capabilities after being transmitted over OSNs. In this work,
we aim to design a new framework for generating robust AEs that can survive the
OSN transmission; namely, the AEs before and after the OSN transmission both
possess strong attack capabilities. To this end, we first propose a
differentiable network termed SImulated OSN (SIO) to simulate the various
operations conducted by an OSN. Specifically, the SIO network consists of two
modules: 1) a differentiable JPEG layer for approximating the ubiquitous JPEG
compression and 2) an encoder-decoder subnetwork for mimicking the remaining
operations. Based upon the SIO network, we then formulate an optimization
framework to generate robust AEs by enforcing model outputs with and without
passing through the SIO to be both misled. Extensive experiments conducted over
Facebook, WeChat and QQ demonstrate that our attack methods produce more robust
AEs than existing approaches, especially under small distortion constraints;
the performance gain in terms of Attack Success Rate (ASR) could be more than
60%. Furthermore, we build a public dataset containing more than 10,000 pairs
of AEs processed by Facebook, WeChat or QQ, facilitating future research in the
robust AEs generation. The dataset and code are available at
https://github.com/csjunjun/RobustOSNAttack.git.Comment: 26 pages, 9 figure
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