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Oils are susceptible to oxidation, so encapsulation of them with small molecule gels and proteins through dynamic high-pressure microfluidization is an effective method of protection. In this study, conjugated linoleic acid (CLA)-rich oil-in-water oleogel emulsions (CLA-Og/W) was prepared by dynamic high-pressure microfluidization. By results, when dynamic high-pressure microfluidization pressure was 80 MPa, zeta potential of CLA-Og/W was −38 mV, the droplet size decreased to 91.3 nm. The surface roughness of the CLA-Og/W reduced to a minimum of 2.75 nm at 80 MPa. The surface tension decreased to 38.36 mN/m. The rheological properties showed that the microfluidized CLA-Og/W had favorable viscoelasticity and increased apparent viscosity. In addition, the fatty acid composition of CLA-Og/W did not change significantly compared to CLA-safflower seed oil. After 14 d of storage at 60 °C, the CLA content of CLA-Og/W was 40.19%. Consequently, CLA-Og/W treated with dynamic high-pressure microfluidization had more stable physicochemical properties and better retention of CLA. • Oleogel emulsion was prepared by dynamic high-pressure microfluidization. • Minimum droplet size obtained at 80 MPa processing pressure. • After 14 d of storage at 60 °C, the conjugated linoleic acid was 40.19%. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are shown to be vulnerable to universal adversarial perturbations (UAP), a single quasi-imperceptible perturbation that deceives the DNNs on most input images. The current UAP methods can be divided into data-dependent and data-independent methods. The former exhibits weak transferability in black-box models due to overly relying on model-specific features. The latter shows inferior attack performance in white-box models as it fails to exploit the model's response information to benign images. To address the above issues, this paper proposes a novel universal adversarial attack to generate UAP with strong transferability by disrupting the model-agnostic features (e.g., edges or simple texture), which are invariant to the models. Specifically, we first devise an objective function to weaken the significant channel-wise features and strengthen the less significant channel-wise features, which are partitioned by the designed strategy. Furthermore, the proposed objective function eliminates the dependency on labeled samples, allowing us to utilize out-of-distribution (OOD) data to train UAP. To enhance the attack performance with limited training samples, we exploit the average gradient of the mini-batch input to update the UAP iteratively, which encourages the UAP to capture the local information inside the mini-batch input. In addition, we introduce the momentum term to accumulate the gradient information at each iterative step for the purpose of perceiving the global information over the training set. Finally, extensive experimental results demonstrate that the proposed methods outperform the existing UAP approaches. Additionally, we exhaustively investigate the transferability of the UAP across models, datasets, and tasks.