Boosting Adversarial Transferability by Achieving Flat Local Maxima

Transfer-based attack adopts the adversarial examples generated on the surrogate model to attack various models, making it applicable in the physical world and attracting increasing interest. Recently, various adversarial attacks have emerged to boost adversarial transferability from different perspectives. In this work, inspired by the fact that flat local minima are correlated with good generalization, we assume and empirically validate that adversarial examples at a flat local region tend to have good transferability by introducing a penalized gradient norm to the original loss function. Since directly optimizing the gradient regularization norm is computationally expensive and intractable for generating adversarial examples, we propose an approximation optimization method to simplify the gradient update of the objective function. Specifically, we randomly sample an example and adopt the first-order gradient to approximate the second-order Hessian matrix, which makes computing more efficient by interpolating two Jacobian matrices. Meanwhile, in order to obtain a more stable gradient direction, we randomly sample multiple examples and average the gradients of these examples to reduce the variance due to random sampling during the iterative process. Extensive experimental results on the ImageNet-compatible dataset show that the proposed method can generate adversarial examples at flat local regions, and significantly improve the adversarial transferability on either normally trained models or adversarially trained models than the state-of-the-art attacks.Comment: 17 pages, 5 figures, 6 table

Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high success rates in the white-box setting, they also exhibit weak transferability in the black-box setting. Recently, various methods have been proposed to improve adversarial transferability, in which the input transformation is one of the most effective methods. In this work, we notice that existing input transformation-based works mainly adopt the transformed data in the same domain for augmentation. Inspired by domain generalization, we aim to further improve the transferability using the data augmented from different domains. Specifically, a style transfer network can alter the distribution of low-level visual features in an image while preserving semantic content for humans. Hence, we propose a novel attack method named Style Transfer Method (STM) that utilizes a proposed arbitrary style transfer network to transform the images into different domains. To avoid inconsistent semantic information of stylized images for the classification network, we fine-tune the style transfer network and mix up the generated images added by random noise with the original images to maintain semantic consistency and boost input diversity. Extensive experimental results on the ImageNet-compatible dataset show that our proposed method can significantly improve the adversarial transferability on either normally trained models or adversarially trained models than state-of-the-art input transformation-based attacks. Code is available at: https://github.com/Zhijin-Ge/STM.Comment: 10 pages, 2 figures, accepted by the 31st ACM International Conference on Multimedia (MM '23

Quartz Enhanced Photoacoustic Detection Based on an Elliptical Laser Beam

A quartz enhanced photoacoustic spectroscopy (QEPAS) sensor system based on an elliptical laser beam for trace gas detection was demonstrated. A Powell lens was exploited to shape the circular laser beam into an elliptical laser beam for the full utilization of the quartz tuning fork (QTF) prong spacing. Based on the finite element modeling (FEM) simulation software COMSOL, the distribution of acoustic pressure on QTF prongs with different beam shapes was simulated theoretically. The experimental results showed that the QEPAS signal based on the elliptical laser beam had a 1.4-fold improvement compared with the circular laser beam, resulting in a minimum detection limit of 418.6 ppmv and the normalized noise equivalent absorption (NNEA) of 1.51 × 10−6 cm−1 W/√Hz at atmospheric pressure

Quartz-enhanced photoacoustic NH3 sensor exploiting a large-prong-spacing quartz tuning fork and an optical fiber amplifier for biomedical applications

A sensor system for exhaled ammonia (NH3) monitoring exploiting quartz-enhanced photoacoustic spectroscopy (QEPAS) was demonstrated. An erbium-doped fiber amplifier (EDFA) with an operating frequency band targeting an NH3 absorption line falling at 1531.68 nm and capable to emit up to 3 W of optical power was employed. A custom T-shaped grooved QTF with prong spacing of 1 mm was designed and realized to allow a proper focusing of the high-power optical beam exiting the EDFA between the prongs. The performance of the realized sensor system was optimized in terms of spectrophone parameters, laser power and modulation current, resulting in a NH3 minimum detectable concentration of 14 ppb at 1 s averaging time, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 8.15 x 10-9 cm- 1 W/root Hz. Continuous measurements of the NH3 level exhaled by 3 healthy volunteers was carried out to demonstrate the potentiality of the developed sensor for breath analysis applications

Ppb-level NH3 photoacoustic sensor combining a hammer-shaped tuning fork and a 9.55µm quantum cascade laser

We present a quartz enhanced photoacoustic spectroscopy (QEPAS) gas sensor designed for precise monitoring of ammonia (NH3) at ppb-level concentrations. The sensor is based on a novel custom quartz tuning fork (QTF) with a mid-infrared quantum cascade laser emitting at 9.55 µm. The custom QTF with a hammer-shaped prong geometry which is also modified by surface grooves is designed as the acoustic transducer, providing a low resonance frequency of 9.5 kHz and a high-quality factor of 10263 at atmospheric pressure. In addition, a temperature of 50 °C and a large gas flow rate of 260 standard cubic centimeters per minute (sccm) are applied to mitigate the adsorption and desorption effect arising from the polarized molecular of NH3. With 80-mW optical power and 300-ms lock-in integration time, the detection limit is achieved to be 2.2 ppb which is the best value reported in the literature so far for NH3 QEPAS sensors, corresponding to a normalized noise equivalent absorption coefficient of 1.4 × 10−8 W cm−1 Hz−1/2. A five-day continuous monitoring for atmospheric NH3 is performed, verifying the stability and robustness of the presented QEPAS-based NH3 sensor

Compact quartz-enhanced photoacoustic sensor for ppb-level ambient NO2 detection by use of a high-power laser diode and a grooved tuning fork

A compact quartz-enhanced photoacoustic sensor for ppb-level ambient NO2 detection is demonstrated, in which a high-power blue laser diode module with a small divergence angle was employed to take advantages of the directly proportional relationship between sensitivity and power, hence improving the detection sensitivity. In order to extend the stability time, a custom grooved quartz tuning fork with 800-um prong spacing is employed to avoid complex signal balance and/or optical spatial filter components. The sensor performance is optimized and assessed in terms of optical coupling, power, gas flow rate, pressure, signal linearity and stability. A minimum detectable concentration (1 sigma) of 7.3 ppb with an averaging time of 1 s is achieved, which can be further improved to be 0.31 ppb with an averaging time of 590 s. Continuous measurements covering a five-day period are performed to demonstrate the stability and robustness of the reported NO2 sensor system

Tumor-Homing and Immune-Reprogramming Cellular Nanovesicles for Photoacoustic Imaging-Guided Phototriggered Precise Chemoimmunotherapy

Many studies have focused on developing effective therapeutic strategies to selectively destroy primary tumors, eliminate metastatic lesions, and prevent tumor recurrence with minimal side effects on normal tissues. In this work, we synthesized engineered cellular nanovesicles (ECNVs) with tumor-homing and immune-reprogramming functions for photoacoustic (PA) imaging-guided precision chemoimmunotherapy. M1-macrophage-derived cellular nanovesicles (CNVs) were loaded with gold nanorods (GNRs), gemcitabine (GEM), CpG ODN, and PD-L1 aptamer. The good histocompatibility and tumor-homing effect of CNVs improved drug retention in the bloodstream and led to their enrichment in tumor tissues. Furthermore, the photothermal ability of GNRs enabled PA imaging-guided drug release. GEM induced tumor immunogenic cell death (ICD), and CpG ODN promoted an immune response to the antigens released by ICD, leading to long-term specific antitumor immunity. In addition, the PD-L1 aptamer relieved the inhibitory effect of the PD1/PD-L1 checkpoint on CD8+ T-cells and augmented the immunotherapeutic effect. The synergistic innate and adaptive immune responses enhanced the antitumor effect of ECNVs. In summary, this nanoplatform integrates local targeted photothermal therapy with extensive progressive chemotherapy and uses ICD to reshape the immune microenvironment for tumor ablation