Out-of-distribution (OOD) detection remains a key challenge preventing the rollout of key AI technologies like autonomous vehicles into the mainstream as classifiers trained on in-distribution (ID) data are unable to gracefully handle OOD data. While OOD detection remains an active area of research, current post-hoc methods often suffer from limited separability between ID and OOD, and outlier exposure-based methods lack generalisation to unseen outlier types. We present CRAFT, a fine-tuning approach for arming pre-trained classifiers against OOD inputs without requiring access to outliers. The key insight that underpins our approach is that during pre-training, classifiers implicitly learn a ranking across the ID classes that is not respected by OOD data. Therefore, a form of fine-tuning without outliers of a pre-trained classifier can sharpen the rank order of the classes, making them sensitive to the presence of OOD data. Furthermore, the fine-tuned model does not impact the ability of the classifier to correctly classify ID inputs to their respective classes. Experiments on CIFAR-10, CIFAR-100, and ImageNet-200 demonstrate that CRAFT outperforms 33 existing methods, particularly in the more challenging near-OOD detection, as well as in overall OOD detection consistency and ID classification accuracy.

We investigate the vulnerability of inputs in an adversarial setting and demonstrate that certain samples are more susceptible to adversarial perturbations compared to others. Specifically, we employ a simple yet effective approach to quantify the adversarial vulnerability of inputs, which relies on the clipped gradients of the loss with respect to the input. Our observations indicate that inputs with a low percentage of zero gradient components tend to be more vulnerable to attacks. These findings are supported by a theoretical explanation on a linear model and empirical evidence on deep neural networks. Across all datasets we tested, we find that inputs with the lowest zero gradient percentage, on average, exhibit 34.5% more susceptibility to adversarial attacks than randomly selected inputs. Additionally, we demonstrate that the zero gradient percentage, as a metric, transfers across different model architectures. Finally, we propose a novel black-box attack pipeline that enhances the efficiency of conventional query-based black-box attacks and show that input pre-filtering based on Zero Gradient Percentage can boost the attack success rates, particularly under low perturbation levels. On average, across all datasets we test, our approach outperforms the conventional shadow model-based and query-based black-box attack pipelines by 44.9% and 30.4%, respectively.

Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information, spreading malware, or simply to increase their advertisement revenue. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted to be published in app markets. We show that for the problem of counterfeit detection, a novel approach of combiningcontent embeddingsandstyle embeddings(given by the Gram matrix of CNN feature maps) outperforms the baseline methods for image similarity such as SIFT, SURF, LATCH, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that, content, style, and combined embeddings increaseprecision@kandrecall@kby 10-15 percent and 12-25 percent, respectively when retrieving five nearest neighbours. Second specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12 and 14 percent increase inprecision@kandrecall@k, respectively compared to the baseline methods. We also show that adding text embeddings further increases the performance by 5 and 6 percent in terms ofprecision@kandrecall@k, respectively when$k$is five. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a conservative assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing a strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning algorithms. Analysis of the overall results suggests the possibility of adopting a real-world training for board games which need precise mechanical actuation beyond simulations.

Counterfeit apps impersonate existing popular apps in attempts to misguide users. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. In this paper, we propose a novel approach of combining content embeddings and style embeddings generated from pre-trained convolutional neural networks to detect counterfeit apps. We present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 apps. Under conservative assumptions, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.