detector

detect_torchscript.py

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+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import sys
+import numpy as np
+from scipy.special import betainc
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+msg_decoder_path = sys.argv[3]
+img_path = sys.argv[1]
+key = int(sys.argv[2])
+
+transform_imnet = transforms.Compose([
+        transforms.ToTensor(),
+#        transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])
+    transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])
+    ])
+
+img = Image.open(sys.argv[1]).convert("RGB").resize((256, 256), Image.BICUBIC)
+
+img = transform_imnet(img).unsqueeze(0).to(device)
+print("img.min", img.min())
+print("img.max", img.max())
+print("img.shape", img.shape)
+
+msg_decoder = torch.jit.load(msg_decoder_path).to(device)
+msg_decoder.eval()
+with torch.no_grad():
+    dec = msg_decoder(img)[0].cpu().numpy()
+#print("dec = ", dec)
+print("dec = ", dec.shape)
+
+msg = np.random.default_rng(seed=key).standard_normal(256)
+msg = msg / np.sqrt(np.dot(msg, msg))
+print("dec.dec", dec.dot(dec))
+print("msg.msg", msg.dot(msg))
+print("dec.msg", dec.dot(msg))
+
+cos_angle = dec.dot(msg)
+pfa = betainc((256 - 1) * 0.5, 0.5, 1 - cos_angle*cos_angle)
+print("pfa = ", pfa)