![]() However, the reports written after this were not very detailed and did not capture the full extent of OSAMiner’s capabilities. The reason was that the researchers were unable to retrieve the malware’s full code. #MACOS MALWARE YEARS RUNONLY APPLESCRIPTS DETECTION FULL# It used nested run-only AppleScript files to retrieve its malicious code across different stages at the time. When the users installed their pirated software, the disguised installers would download and run a run-only AppleScript. It would then download and run a second run-only AppleScript and then run another third/final one.īecause the run-only AppleScript is received in a compiled state (the source code is not readable by humans), security researchers’ analysis was not easy. #MACOS MALWARE YEARS RUNONLY APPLESCRIPTS DETECTION CODE# Phil Stokes, a macOS malware researcher at SentinelOne, published the attack’s full-chain with past and present OSAMiner campaigns and IOCs (Indicators of Compromise). The hope for this team of researchers is that they can crack the mystery around this clever malware. #MACOS MALWARE YEARS RUNONLY APPLESCRIPTS DETECTION CODE#.#MACOS MALWARE YEARS RUNONLY APPLESCRIPTS DETECTION SOFTWARE#.#MACOS MALWARE YEARS RUNONLY APPLESCRIPTS DETECTION FULL#.Vis_util.visualize_boxes_and_labels_on_image_array( # Visualization of the results of a detection. ![]() (boxes, scores, classes, num_detections) = n( Num_detections = detection_graph.get_tensor_by_name('num_detections:0') Scores = detection_graph.get_tensor_by_name('detection_scores:0')Ĭlasses = detection_graph.get_tensor_by_name('detection_classes:0') # Score is shown on the result image, together with the class label. # Each score represent how level of confidence for each of the objects. # Each box represents a part of the image where a particular object was detected.īoxes = detection_graph.get_tensor_by_name('detection_boxes:0') Image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') Image_np_expanded = np.expand_dims(image_np, axis=0) # Expand dimensions since the model expects images to have shape: With tf.Session(graph=detection_graph) as sess: Label_map = label_map_util.load_labelmap(PATH_TO_LABELS)Ĭategories = label_map_nvert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)Ĭategory_index = label_map_util.create_category_index(categories) Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine # Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Tf.import_graph_def(od_graph_def, name='') Od_graph_def.ParseFromString(serialized_graph) With tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: # Load a (frozen) Tensorflow model into memory. If 'frozen_inference_graph.pb' in file_name: ![]() Opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE) If not os.path.exists(MODEL_NAME + '/frozen_inference_graph.pb'): PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') # List of the strings that is used to add correct label for each box. PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' This is the actual model that is used for the object detection. See the () for a list of other models that can be run out-of-the-box with varying speeds and accuracies. # By default we use an "SSD with Mobilenet" model here. # Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new. import numpy as npįrom utils import visualization_utils as vis_util Is there any way to remove objects from the model or filter out objects from the person class? I'm using tensorflow's pretrained model and a code example to perform object detection on a webcam. I've been trying to use tensorflow's object detection to try and set up a decent presence detection.
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