156 research outputs found
Physical Invisible Backdoor Based on Camera Imaging
Backdoor attack aims to compromise a model, which returns an adversary-wanted
output when a specific trigger pattern appears yet behaves normally for clean
inputs. Current backdoor attacks require changing pixels of clean images, which
results in poor stealthiness of attacks and increases the difficulty of the
physical implementation. This paper proposes a novel physical invisible
backdoor based on camera imaging without changing nature image pixels.
Specifically, a compromised model returns a target label for images taken by a
particular camera, while it returns correct results for other images. To
implement and evaluate the proposed backdoor, we take shots of different
objects from multi-angles using multiple smartphones to build a new dataset of
21,500 images. Conventional backdoor attacks work ineffectively with some
classical models, such as ResNet18, over the above-mentioned dataset.
Therefore, we propose a three-step training strategy to mount the backdoor
attack. First, we design and train a camera identification model with the phone
IDs to extract the camera fingerprint feature. Subsequently, we elaborate a
special network architecture, which is easily compromised by our backdoor
attack, by leveraging the attributes of the CFA interpolation algorithm and
combining it with the feature extraction block in the camera identification
model. Finally, we transfer the backdoor from the elaborated special network
architecture to the classical architecture model via teacher-student
distillation learning. Since the trigger of our method is related to the
specific phone, our attack works effectively in the physical world. Experiment
results demonstrate the feasibility of our proposed approach and robustness
against various backdoor defenses
Towards Deep Network Steganography: From Networks to Networks
With the widespread applications of the deep neural network (DNN), how to
covertly transmit the DNN models in public channels brings us the attention,
especially for those trained for secret-learning tasks. In this paper, we
propose deep network steganography for the covert communication of DNN models.
Unlike the existing steganography schemes which focus on the subtle
modification of the cover data to accommodate the secrets, our scheme is
learning task oriented, where the learning task of the secret DNN model (termed
as secret-learning task) is disguised into another ordinary learning task
conducted in a stego DNN model (termed as stego-learning task). To this end, we
propose a gradient-based filter insertion scheme to insert interference filters
into the important positions in the secret DNN model to form a stego DNN model.
These positions are then embedded into the stego DNN model using a key by side
information hiding. Finally, we activate the interference filters by a partial
optimization strategy, such that the generated stego DNN model works on the
stego-learning task. We conduct the experiments on both the intra-task
steganography and inter-task steganography (i.e., the secret and stego-learning
tasks belong to the same and different categories), both of which demonstrate
the effectiveness of our proposed method for covert communication of DNN
models.Comment: 8 pages. arXiv admin note: text overlap with arXiv:2302.1452
Bootstrapping Multi-view Representations for Fake News Detection
Previous researches on multimedia fake news detection include a series of
complex feature extraction and fusion networks to gather useful information
from the news. However, how cross-modal consistency relates to the fidelity of
news and how features from different modalities affect the decision-making are
still open questions. This paper presents a novel scheme of Bootstrapping
Multi-view Representations (BMR) for fake news detection. Given a multi-modal
news, we extract representations respectively from the views of the text, the
image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert
networks (iMMoE) are proposed for feature refinement and fusion.
Representations from each view are separately used to coarsely predict the
fidelity of the whole news, and the multimodal representations are able to
predict the cross-modal consistency. With the prediction scores, we reweigh
each view of the representations and bootstrap them for fake news detection.
Extensive experiments conducted on typical fake news detection datasets prove
that the proposed BMR outperforms state-of-the-art schemes.Comment: Authors are from Fudan University, China. Under Revie
Generative Steganography Diffusion
Generative steganography (GS) is an emerging technique that generates stego
images directly from secret data. Various GS methods based on GANs or Flow have
been developed recently. However, existing GAN-based GS methods cannot
completely recover the hidden secret data due to the lack of network
invertibility, while Flow-based methods produce poor image quality due to the
stringent reversibility restriction in each module. To address this issue, we
propose a novel GS scheme called "Generative Steganography Diffusion" (GSD) by
devising an invertible diffusion model named "StegoDiffusion". It not only
generates realistic stego images but also allows for 100\% recovery of the
hidden secret data. The proposed StegoDiffusion model leverages a non-Markov
chain with a fast sampling technique to achieve efficient stego image
generation. By constructing an ordinary differential equation (ODE) based on
the transition probability of the generation process in StegoDiffusion, secret
data and stego images can be converted to each other through the approximate
solver of ODE -- Euler iteration formula, enabling the use of irreversible but
more expressive network structures to achieve model invertibility. Our proposed
GSD has the advantages of both reversibility and high performance,
significantly outperforming existing GS methods in all metrics.Comment: Draft for ACM-mm 2023.Shall not be reproduced without permission,
rights reserved
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection
The widespread use of face retouching filters on short-video platforms has
raised concerns about the authenticity of digital appearances and the impact of
deceptive advertising. To address these issues, there is a pressing need to
develop advanced face retouching techniques. However, the lack of large-scale
and fine-grained face retouching datasets has been a major obstacle to progress
in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and
fine-grained face retouching dataset that contains over half a million
conditionally-retouched images. RetouchingFFHQ stands out from previous
datasets due to its large scale, high quality, fine-grainedness, and
customization. By including four typical types of face retouching operations
and different retouching levels, we extend the binary face retouching detection
into a fine-grained, multi-retouching type, and multi-retouching level
estimation problem. Additionally, we propose a Multi-granularity Attention
Module (MAM) as a plugin for CNN backbones for enhanced cross-scale
representation learning. Extensive experiments using different baselines as
well as our proposed method on RetouchingFFHQ show decent performance on face
retouching detection. With the proposed new dataset, we believe there is great
potential for future work to tackle the challenging problem of real-world
fine-grained face retouching detection.Comment: Under revie
Synthesis of a Bi2O2CO3/ZnFe2O4 heterojunction with enhanced photocatalytic activity for visible light irradiation-induced NO removal
Although bismuth subcarbonate (Bi2O2CO3), a member of the Aurivillius-phase oxide family, is a promising photocatalyst for the removal of gaseous NO at parts-per-billion level, the large band gap of this material restricts its applications to the UV light region. The above problem can be mitigated by heterojunction fabrication, which not only broadens the light absorbance range, but also inhibits the recombination of photogenerated charge carriers. Herein, we implement this strategy to fabricate a novel Bi2O2CO3/ZnFe2O4 photocatalyst for NO removal under visible light irradiation and authenticate the formation of the above p-n heterojunction using an array of analytical techniques. Notably, the above composite showed activity superior to those of its individual constituents, and the underlying mechanisms of this activity enhancement were probed by density functional theory calculations and photocurrent measurements. Elevated electron/hole separation efficiency caused by the presence of an internal electric field at the Bi2O2CO3/ZnFe2O4 interface was identified as the main reason of the increased photocatalytic activity, with the main active species were determined as center dot O-2(-) and center dot OH by electron spin resonance spectroscopy. Finally, cytotoxicity testing proved the good biocompatibility of Bi2O2CO3/ZnFe2O4. Thus, this work presents deep insights into the preparation and use of a green p-n heterojunction catalyst in various applications
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